Memory anization forStream-Based Reconfigu
心理学专业英语memory
• Definition:
memory systems have large capacity but very short duration.
• Two main types: 1. iconic memory（映像记忆）:momentarily pres 5 : sensory memory
• Questions:1.what is sensory memory?
• 2.The duration of iconic memory and echoic memory.
sensory memory refers to the initial, momentary recording of information in our sensory systems. Sensory
• Questions:what is long-term memory? • Definition:
• Long-term memroy can store much larger quantities of information for potentially unlimited duration (sometimes a whole life span) . Long-term memory encodes information semantically.
2.echoic memory（回声记忆）:holds auditory stimuli
• • • •
Paragraph 6:short-term memory
Questions: 1.what is short-term memory? 2.how can we increase the memory capacity? Definition: Short-term memory (STM) has a limited capacity and lasts only briefly without rehearsal. Maintenance rehearsal can extend the presence of material in STM indefinitely. 短时记忆容量有限，在不复述的情况下，保存时间很短。保持复 述可以在一定程度上延长短时记忆材料的保留。 STM capacity can be increased by chunking unrelated items into meaningful groups. 可以通过把无意义的项目加以组块，构成有意义的族群，从而增 加短时记忆的容量。
记忆提取与储存
Eric R. Kandel
美国科学家Eric R. Kandel由于在研究 脑细胞间信号的相互传递方面获得 了重大发现，与其他两位科学家获 得了2000年诺贝尔生理学或医学奖。 Ericቤተ መጻሕፍቲ ባይዱKandel在哈佛大学的本科念的并 不是生物，而是文学史，但是对于 精神分析方面书籍的阅读使他对于 人类的精神世界产生了极大的兴趣， 于是他毕业之后选择了Harry Grundfest的实验室，从此正式开始 了自己在神经生物学领域的探索。
Figure 8:长时记忆突触变化机制示意图
The research of Explicit memory
• 今年的诺贝尔诺贝尔生理学或医学奖授予了John O’Keefe、May-Britt Moser、 Edvard Moser三位科学家，以表彰他们发现大脑定位系统细胞的研究。1971 年， John O’Keefe在海马体中发现了位置细胞。34年后Moser夫妇识别出另 一种神经细胞，他们将其称为“网格细胞”，这些细胞产生一种坐标体系， 从而让精确定位与路径搜寻成为可能。他们随后进行的研究揭示了位置细胞 以及网格细胞是如何让定位与导航成为可能的。
Figure 3: 海兔背面图
How memories are made？
在动物中存在这样三种非常普遍的行为学 现象 ：习惯化（habituation），敏感化 (sensitization)，经典条件反射(classical conditioning)。 通过对海兔缩鳃反应这三种行为学现象的 学习，Kandel的发现验证了Cajal的假说： 经验改变了已经存在的化学连接的强度和 效率。也许遗传和发育因素只能通过先天 形成的神经通道控制动物的行为潜能，而 环境和学习能够改变神经通道的传递效能， 从而带来新的行为形式。 Figure 4: 海兔缩鳃反应行为学学习图 通过还原法研究，Kandel总结出几条学习和记忆的细胞生物学原则。 首先，突触联系强度的变化足以改变原有的神经网络结构及其信息加工能力。 其次，不同形式的学习可以以完全相反的方式改变两神经元间的突触联系——强化或弱化。 第三，短时记忆存储的时间长短取决于突触强化或弱化的时间长短。 第四，突触强度的调控方式分为同突触和异突触两种。
英汉口译教程
Methods to Improve Memory
• • • • • • Organization Repetition Visualization Chunking Association Note-taking
Organization
• Sequential – chronological; cause/effect; building to climax. • Directional – north/south & east/west; front/back; left/right; bottom/up or top/down, etc. • Transitional (connective) – relational words or phrases used to indicate qualitative change over time • Component (part/whole) – classification by category or concept • Relevance— topic sentence or supporting sentences
中餐常见的翻译方法
• 烹饪方法 cookery 煮 boiling 煲／炖 stewing 煎 frying 炒 stir-frying 爆 quick-frying 炸 deep-frying 熏 smoking 烤 roasting/barbecuing 蒸 steaming 红烧 braising with soy sauce
students approach to learning(学习品质)
ISSN 1306-3065 Copyright © 2006-2013 by ESER, Eurasian Society of Educational Research. All Rights Reserved.Student Approaches to Learning in Physics – Validityand Exploration Using Adapted SPQManjula Devi Sharma*1, Chris Stewart 1, Rachel Wilson 1, and MuhammedSait Gökalp 2Received 22 December 2012; Accepted 9 March 2013Doi: 10.12973/ijese.2013.203aAbstract: The aim of this study was to investigate an adaptation of the Study ProcessesQuestionnaire for the discipline of physics. A total of 2030 first year physics students at an Australian metropolitan university completed the questionnaire over three different year cohorts. The resultant data has been used to explore whether the adaptation of the questionnaire is justifiable and if meaningful interpretations can be drawn for teaching and learning in the discipline. In extracting scales for deep and surface approaches to learning, we have excised several items, retaining an adequate subset. Reflecting trends in literature, our deep scale is very reliable while the surface scale is not so reliable. Our results show that the behaviour of the mean scale scores for students in different streams in first year physics is in agreement with expectations. Furthermore, different year cohort performance on the scales reflects changes in senior high school syllabus. Our experiences in adaptation, validation and checking for reliability is of potential use for others engaged in contextualising the Study Processes Questionnaire, and adds value to the use of the questionnaire for improving student learning in specific discipline areasKeywords: Student approaches to learning, learning in disciplines, university physics education1University of Sydney, Australia* Corresponding author. Sydney University Physics Education Research group, School of Physics, University of Sydney, NSW 2006, Australia. Email: sharma@.au 2Dumlupınar University, TurkeyIntroductionSince the mid 1960’s a series of inventories exploring student learning in highereducation have been developed based on learning theories, educational psychology and study strategies. For reviews of the six major inventories see Entwistle and McCune (2004)International Journal of Environmental & Science EducationVol. 8, No. 2, April 2013, 241-253242C o p y r i g h t © 2006-2013 b y E S E Rand Biggs (1993a). As can be seen from the reviews, these inventories have two common components. One of these components is related to study strategies and the other one is about cognitive processes. Moreover, these inventories usually have similar conceptual structures and include re-arrangement of the items (Christensen et al., 1991; Wilson et al., 1996).In the current study, as one of these inventories, The Study Processes Questionnaire (SPQ) has been selected to be adapted for use in physics. The SPQ is integrated with the presage-process-product model (3P model) of teaching and learning (Biggs, 1987). Several studies have successfully used the SPQ across different culture s and years to compare students’ approaches in different disciplines (Gow et al., 1994; Kember & Gow, 1990; Skogsberg & Clump, 2003; Quinnell et al., 2005; Zeegers, 2001). Moreover, several other researchers used modified version of the SPQ at their studies (Crawford et al. 1998a,b; Fox, McManus & Winder, 2001; Tooth, Tonge, & McManus, 1989; Volet, Renshaw, & Tietzel, 1994). For example, Volet et al (2001) used a shortened SPQ included 21 items to assess cross cultural differences. Fox et al (2001) modified the SPQ and tested its structure with confirmatory factor analysis. In their study the modified version of the SPQ had 18 items, and this shortened version had same factor structure as the original SPQ. In another study, Crawford et al. (1998a, b) adapted the SPQ for the discipline of mathematics. That adapted questionnaire was named as Approaches to Learning Mathematics Questionnaire.Three different approaches of the students to learning are represented in the SPQ: surface, deep, and achieving approaches. Idea of approaches to learning was presented by Marton and Säljö (1976) and further discussed by several other researchers (eg. Biggs, 1987; Entwistle & Waterston, 1988). Basically, surface approach indicates that the students’ motivation to learn is only for external consequences such as getting the appreciation of the teacher. More specifically, it is enough to fulfill course requirements for the students with surface approach. On theother hand, a deep approach to learning indicates that the motivation is intrinsic. This approach involves higher quality of learning outcomes (Marton & Säljö, 1976; Biggs, 1987). Students with deep approach to learning try to connect what they learn with daily life and they examine the content of the instruction more carefully. On the other hand, achieving approach is about excelling in a course by doing necessary things to have a good mark. However, current study is not focused on this approach. Only the first two approaches were included in the adapted SPQ.Inventories like the SPQ are used in higher education because of several reasons. Such inventories can help educators to evaluate teaching environments (Biggs, 1993b; Biggs, Kember, & Leung, 2001). Moreover, with the use of these inventories, university students often relate their intentions and study strategies for a learning context in a coherent manner. On the other hand, the SPQ is not a discipline specific inventory. It can be used across different disciplines. However, in a research study, if the research questions are related with the common features of learning and teaching within 3P model framework, the SPQ can be used satisfactorily for all disciplines. But, a discipline specific version of the SPQ is required if resolution of details specific to a discipline area is necessary for the research questions. Moreover, in order to reduce systematic error and bias that can be resulted from students in different discipline areas; a discipline specific version may be required. As a community of educators, we are aware that thinking, knowing, and learning processes can differ across discipline areas. A direct consequence of this acknowledgement is the need to understand and model learning in specific discipline areas, such as by adapting the SPQ. However, for the theoretical framework to be valid the conceptual integrity of the inventory must be maintained.This paper reports on how the SPQ has been adapted for physics. The teaching context is first year physics at a research focused Australian university where students are grouped according to differing senior243Student approaches to learning in physicsC o p y r i g h t © 2006-2013 b y E S E Rhigh school experiences into Advanced, Regular, and Fundamentals streams.We report on the selection of items for the deep and surface scales and reliability and validity analyses. A comparison of the Advanced, Regular and Fundamentals streams is carried out to ensure that interpretations associated with the deep and surface scales are meaningful. This is a stage of a large-scale project. The project aims to understand and improve student learning based on the deep and surface approaches to learning inherent in the 3P model (Marton & Säljö, 1976; Biggs, 1987).The studyAs mentioned before, The SPQ has been designed for higher education; however, this questionnaire is not discipline specific. Therefore, in this study, we adapted the SPQ to physics for the following reasons: (1) The first year students have confusions about university studies when they come to university (White et al., 1995). This can lead to misinterpretation of the items. However, specific items related to physics can reduce these misinterpretations. For example students enrolled in a general science degree would view questions related to employment differently to those in professional degrees, and the students we have surveyed are from a range of degree programs. (2) In order to compare the students from the different discipline areas, we need discipline specific inventories. (3) We believe that there are contentious items in the original SPQ and aspects that are specific to physics. For example the use of “truth” in the following item was strongly challenged by a group of physicists validating the questionnaire. While I realize that truth is forever changing as knowledge is increasing, I feel compelled to discover what appears to me to be the truth at this time (Biggs, 1987, p. 132).The item was changed to the following, more in line with the post-positivist paradigm and agreeable to physicists.While I realize that ideas are always changing as knowledge is increasing, I feel a need to discover for myselfwhat is understood about the physical world at this time.One could argue that this is an issue of clarifying the item rather than being specific to physics. However, to our knowledge the clarity of this item has not been debated in literature.Just after we commenced this study in 2001, we became aware that Biggs et al (2001) had produced a revised Study Processes Questionnaire (R-SPQ- 2F). However, it was too late for our study and we did not switch midway. There are four main differences between the SPQ and the R-SPQ-2F; first, the removal of all items on employment after graduation; second, increased emphasis on examination; third, removal of words that imply specificity; and fourth exclusion of the contentious achieving factor identified by Christensen et al., 1991. We focus on the deep and surface approaches and not on the strategy and motive sub-scales as these are not pertinent to our larger study. The SPQ deep and surface scales, in particular, have been shown to be robust (see for example Burnett & Dart, 2000).The participant of the current study was from a university in New South Wales, Australia. Students are provided three basic physics units in the School during their first semester of university: Fundamentals, Regular or Advanced. Students are divided into these three groups of physics units based on their senior high school physics backgrounds. The students from the Fundamentals unit have done no physics in senior high school or have done poorly. On the other hand, in the Regular unit, there were the students had scored high grades in senior high school physics. The last unit, the Advanced unit, is suitable for those who have done extremely well overall in physics during all their years in senior high school.The three physics units that students can register in are for the degree programs in Engineering, Medical Science and Arts. Students who intend to major in physics as well as postgraduate physics students are selected from those enrolled in all three basic physics course in their first semester at university. The largest proportion of students of physics major is from the Advanced244C o p y r i g h t © 2006-2013 b y E S E Rstream, followed by those in the Regular stream, and finally the Fundamentals stream. The data was collected from these streams from 2001 to 2004. From 2001 to 2004, the high school physics syllabi and assessment system was changed in the state of New South Wales in Australia. The details of the changes can be seen in Binnie (2004). Due to these changes, the 2004 cohort of students in this study were instructed using a different curriculum.Within the above context, we have adapted the SPQ to generate a Study Processes Questionnaire for Physics (SPQP). The research questions addressed in this paper are as follows.(a) How do the factor solutions for the SPQP compare with those of the SPQ? (b) Is the SPQP reliable and valid?(c) Are the scales robust enough to reflect detail in senior high school syllabus change? The answers to the research questions will determine if the SPQP is a reliable and valid measure of student approaches to learning physics in our context.MethodRevising the items for the SPQPWe have adapted the SPQ by simply inserting the word “physics” in some items and making substantial changes to others. The adaptations are based on our experiences of student responses to open-ended questions and discipline knowledge, and have been extensively discussed amongst a group of physics educators. The adaptations are of the types listed below. (See appendix A for all items and the types of adaptations.) Type 0: No changeType 1: A simple insertion of terms such as “physics”, “studying physics”.I find that at times studying gives me afeeling of deep personal satisfaction. I find that at times studying physics gives me a feeling of deep personal satisfaction.Type 2: A substantial change in wording that can change the meaning, without intending to.I usually become increasingly absorbed in my work the more I do. When studying physics, I become increasingly absorbed in my work the more I do.Type 3: An intentional change in meaning.My studies have changed my views about such things as politics, my religion, and my philosophy of life. My studies in physics have challenged my views about the way the world works.The number of items corresponding to each Type of change is displayed in Table 1, as are the number of items selected from each Type for inclusion in the SPQP. Type 1 items were more useful in generating the items used in the SPQP.Administering the SPQPThe SPQP was administered at the beginning of the first semester to students in the Advanced, Regular and Fundamentals streams in 2001, 2002 and 2004, respectively. On the questionnaire, the students were requested to indicate their level of agreement with each item on a Likert scale with the options of Strongly Disagree, Disagree, Neutral, Agree, and Strongly Agree . Response rates of 2001, 2002, and 2004 cohorts was 95%, 65%, and 85%, respectively. Except 2002 cohort, the response rates were satisfactory. The main reasons of the lower response rate of 2002 cohort were the changes in class organization and questionnaire administration. Over these245Student approaches to learning in physicsC o p y r i g h t © 2006-2013 b y E S E Rthree years, a total of 2030 first year physics student were responded the SPQP: 63 percent of students in the Fundamentals stream was female, and about 30 percent of them was females in the Regular and Advanced streams. Nevertheless, the three streams are similar in other respects. The sample size of 2030 is large enough to access the natural variance within the diverse population. However, due to missing answers some of the cases were excluded from the analysis. These exclusions were only about 3% of the whole sample. Therefore, we can say that this missing data did not affect the overall results. Data Analysis MethodsFollowing analyses were carried out to answer research questions.(a) Both exploratory and confirmatory factor analyses were performed to validate the two-factor solution: the deep and surface scales. (b) Cronbach’s alpha coefficients were calculated to determine the reliability for the deep and surface scales for the complete data set and for each stream.(c) ANOVA and boxplots were used to determine if the SPQP is able to differentiate between the three streams and changes in syllabus.ResultsFactor analysisIn order to gain construct related evidence for the validity of the SPQP, exploratory and confirmatory factor analysis were conducted. Exploratory factor analysis (EFA) was carried out by using principal components as factor extraction method with quartimax, an orthogonal rotation. The complete data set was included in this analysis. Before proceeding to interpret the results, each item was checked for normality and sphericity. In order to check multicollinearity, the correlation matrix was examined. In terms of multicollinearity, we expect the items to be intercorrelated; however, these correlations should not be so high (0.90 or higher), whichcauses to multicollinearity and singularity. The intercorrelation was checked by Bartlett’s test of sphericity. This test showed that the correlation matrix is not an identity matrix. Moreover, multicollinearity was checked with the determinant of the correlation matrix. The determinant was more than 0. This showed that there is no multicollinearity (Field, 2000). Extraction of factors was based on two criteria: Scree test and Kaiser criterion (eigen values). Based on eigen values and the Scree test, two factors, which accounts for 48% of the variance, were extracted. The items with factor loadings of less than .4 were excluded from the further analyses (Field, 2000). Appendix A shows the two-factor solution for all items including loadings. Those that were retained for the SPQP are starred – 10 items form the deep scale and 6 items the surface scale.According to the results of the EFA, we note that the deep scale is in better agreement with Biggs ’s deep scale than the surface scale - there are more “usable” items on the deep scale than on the surface scale.After having results of the EFA, confirmatory factor analysis (CFA) was performed. This second step of the factor analysis helped us to ensure the factor structure of the SPQP (see Figure 1). Maximum likelihood (ML) was used as the method of estimation at the CFA. The results of the study showed that relative chi-square, which is the “chi square/degree of freedom” ratio is 3.1. Moreover, RMSEA and CFI were found to be 0.07 and 0.69 respectively. According to Browne and Cudeck (1992), RMSEA values less than 0.05 indicate close fit and models with values greater than 0.10 should not be employed. Here, RMSEA indicates moderate fit of the model whereas relative chi-square indicates good fit. However, the CFI should be over 0.90 to have good fit. Nonetheless, we can say that the first two indices support this two-factor model of the SPQP and indicate moderate fit.246C o p y r i g h t © 2006-2013 b y E S E RFigure 1. Validated two-factor structure of the SPQP.Reliability of the SPQPCronbach alpha coefficients of each scale were calculated for each stream and whole data. The results are shown in Table 2. It is apparent that the surface scale has the lowest Cronbach alpha coefficients at each stream. Similar findings were also reported at other studies (Biggs, 1987; Biggs et al, 2001; Wilson and Fowler, 2005). The foundational efficacy of these scales, given such low reliability, is questionable. However, in ourstudy higher levels of internal consistency were apparent (lowest α =.61).Comparing reliabilities across streams, students who have less experience with physics report surface approaches more reliably than students with more experience. On the other hand, students who have more experience with physics report deep approaches more reliably than those who have less experience. Considering reliabilities within streams, the Fundamentals students report deep approaches as reliably as surface247Student approaches to learning in physicsC o p y r i g h t © 2006-2013 b y E S E Rapproaches with values greater than 0.80, while the Advanced students report very different reliabilities for the two scales. The trends are not surprising since Advanced students would tend to be more confident in content and study strategies.The above trends also raise the question: Are the persistently low reliabilities noted for the su rface scale due to student ‘behaviours’ or poor items on the inventory? An adequate reliability measure for the surface scale for the Fundamentals stream α > .80, one that is similar in magnitude to that of the deep scale, implies that there is internal consistency amongst the items for each scale for this group of students. We note that the Fundamentals students have experienced junior high school physics, and are doing concurrent science and mathematics subjects at university. University students tend to have high internal coherence among learning components, intentions and study strategies and are able to adapt their ideas of knowledge and study methods to their expectations of studying in a particular context. The internal coherence is demonstrated in the reliability scores. So why are the reliabilities for the surface scale as low as 0.61 for the Advanced stream? Is it because the nature of the surface approach is different for the Advanced and Fundamentals streams, requiring possibly different items? Or is it because the Advanced students adapt their surface approaches in diverse ways, hence report this scale less reliably? Answers to such questions will indeed add to our understanding of student learning.ANOVA and BoxplotsTo determine if the SPQP is able to differentiate between the three streams, item and scale means were compared using one-way ANOVA.When comparing the means of the three streams for each item on the SPQP, the assumption of homogeneity of variances underpinning ANOVA was tested using Mulachy’s test of Sphericity. Items A5, A13 and A25 were excluded from ANOVA because they violated the assumption of sphericity. This does not affect their use on the SPQP scales. The results of ANOVA showed that there is a significant differenceamong the SPQP scores of the students from Fundamentals, Regular, and Advanced streams for both surface and deep scales (p < .05).There is a debate among the researchers to use ANOVA with the ordinal data mainly because of the normality concern. As stated in Glass, Peckham, and Sanders (1972), violation of normality is not fatal to the ANOVA. Moreover, performing ANOVA with the ordinal data, likert type items in this case, is a controversial issue among the researchers. Here, we have a big sample and this surely increases the power of the test. Therefore, failure to meet normality assumption will not affect the results of the ANOVA. Moreover, we performed Kruskal Wallis test as non-parametric alternative of the ANOVA. The results of that test supported the results of the ANOVA given above.Moreover, in order to investigate if SPQP is robust enough to be able to differentiate changes in syllabus even when sum of the items is used instead of factor scores, boxplots were checked (see Figure 2). The boxplots show a sequence for each scale with the first panel representing the factor scores, the second panel the simple sums of the items scores for the SPQP and the third panel the simple sums of all 16 item scores that should have loaded on each scale. We note two important features. First, the three panels representing the deep scale are sufficiently similar implying that if an adaptation such as that in this study is made, the sums of the 10 SPQP item scores, and indeed all 16 items scores provide a reasonable measure of deep approaches to learning. However, this is not so for the surface scale, while the sum of the 6 SPQP item scores provides a reasonable measure of surface approaches to learning, the sum of all 16 items does not do so. This raises concerns regarding the surface scale and is a reflection of the low reliabilities for the scale.DiscussionAs we are particularly interested in issues to do with learning physics, the rationale and manner in which items were modified and the SPQ adapted are discussed in detail. The advantages of adapting an established, well248C o p y r i g h t © 2006-2013 b y E S E RFigure 2. A comparison by stream and year of the deep and surface scales. The boxplots show a vertical sequence for each scale with panel (a) representing the factor scores for the SPQP deep scale and panel (d) those for the SPQP surface scale. Panel (b) represents the simple sum of item scores for the SPQP deep scale and panel (e) those for the SPQP surface scale. Panel (c) represents the simple sum of all 14 item scores that were intended to load on the deep scale and panel (f) those for the surface scale.implemented inventory with a sound theoretical framework, both for itsdevelopment and for its practical use in a teaching environment, are evident in the249Student approaches to learning in physicsC o p y r i g h t © 2006-2013 b y E S E Rmeaningful interpretations of our results summarized below.1. The SPQ items were modified for our context based on our experiences and any changes were extensively discussed amongst a group of physics educators. Ten items were retained for the deep scale and six for the surface. The rejection of items that had anomalous factor loadings could be conceptually justified. This two-factor solution of the SPQP confirmed with the EFA and CFA and supported the factor structure of the original SPQ (Biggs, 1987).2. The trends in reliabilities according to streams are as expected, with students with less experience in physics reporting less reliably on the deep scale and more reliably on the surface scale and vice versa. The issue of low reliabilities of the surface scale for the Advanced stream raises the question of whether Advanced students have more diverse forms of exhibiting surface approaches to learning. Moreover, the issue with the surface scale coincides with the previous studies (Biggs, 1987; Biggs et al, 2001; Wilson and Fowler, 2005).3. Comparisons of deep factor scores, simple sums of the 10 SPQP items and all 16 items suggest that the deep scale is reliable and particularly robust, see Figure 2. The surface factor scores compare well with the simple sums of the 6 SPQP items, but not with all 16 items, suggesting that reliability and validity checks are particularly important for the surface scale. The implication is twofold: first the SPQ is robust when contextualised as shown by reliability scores; and second, the contextualisation did not alter the overall coherency of the inventory as shown by the meaningful interpretations across streams and years. This, together with the conceptual meanings associated with the items, provides confidence that the SPQP is consistent with the theoretical framework of the SPQ.4. Changes in senior high school physics syllabus have impacted on approaches to study in the cohorts sampled in this study. The SPQP can illustrate differences between streams and years. From our study we are confident that the SPQP is a reliable andvalid measure of approaches to learning physics in our context.5. The adaptation of the SPQ into physics adds value to our project findings as it allows us to illustrate physics’ specific detail between the streams. We are confident that features that could have systematically biased the findings have been minimized. Lastly the ways of thinking, learning and knowing in physics are embedded in the larger context of intentions and study methods in higher education.ConclusionWe have adapted the Study Processes Questionnaire into physics and confirmed that a two-factor solution provides two subsets of selected items representing deep and surface approaches to learning. The resulting inventory is called the Study Processes Questionnaire for Physics, or SPQP. Further reliability and validation checks demonstrate that the two-scale SPQP is a useable inventory for our context. Reliabilities for the Advanced, Regular and Fundamentals streams are adequate and the behaviour of the mean scale scores for the three streams is not contradictory to expected student behaviours.The process of adapting the SPQ has provided useful insights into the way physicists interpret the items, and how deep and surface approaches can be conceptualised in physics. The sound theoretical framework and research underpinning the SPQ has added value to the use of questionnaires for understanding student learning in our project. Such contextualised inventories have the potential to provide context-specific understandings of teaching and learning issues and for improving student learning.AcknowledgementsThe authors acknowledge Science Faculty Education Research (SciFER) grants, University of Sydney, and support from staff and students. The authors are grateful to Professor David Boud for his constructive feedback on this paper.。
英语字词的记忆妙法
01
Understanding the principles of memory
The mechanism of memory
Memory is a psychological process of encoding, storage, and retrieval information
Sleep and Memory
The importance of sleep for memory
01
Good sleep helps to consolidate memory and improve
learning outcБайду номын сангаасmes.
Ensure sufficient sleep time
02
Create a pleasant learning atmosphere
When learning English words, try to keep yourself in a joyful state, such as listening to favorite music, watching interesting videos, etc.
Long term memory is divided into episodic and semantic memory
Short term memory has a limited capacity and can hold only a few items at a time
Types of memory
Schedule regular reviews to assess progress and identify areas that require more focus
想要了解的事物英语作文
想要了解的事物英语作文Things I Yearn to Understand The world is an intricate tapestry woven with threads of knowledge, both known and unknown. While I find myself fascinated by the vast amount of information we’ve accumulated as a species, I am acutely aware of the vast, uncharted territories of understanding that lie before me. There are several key areas that spark a deep curiosity within me, areas I yearn to explore and grasp with greater clarity. Firstly, I am captivated by the complex workings of the human mind. The brain, a three-pound universe contained within our skulls, is a marvel of intricate networks and electrochemical signals that give rise to consciousness, emotion, and behavior. How do neurons fire in symphony to create our perceptions of the world? What are the mechanisms behind memory formation and retrieval? How does our unique blend of genetics and environment shape our personalities and predispositions? Unraveling the mysteries of the mind holds the key to understanding the very essence of what makes us human. The vast universe, with its swirling galaxies, enigmatic black holes, and the tantalizing possibility of life beyond Earth, also ignites my imagination. I long to understand the fundamental laws that govern the cosmos, from the delicate dance of subatomic particles to the majestic movements of celestial bodies. What is the true natureof dark matter and dark energy, the unseen forces shaping the universe's evolution? Are we alone in this vast cosmic expanse, or does life, in all its wondrous forms, exist elsewhere? The pursuit of answers to these questions is a quest to understand our place in the grand scheme of existence. Closer to home, the interconnected web of life on our planet fascinates me. The intricate ecosystems teeming with biodiversity, the delicate balance of predator and prey, theintricate cycles of energy and nutrients - these are all testament to the awe-inspiring power of evolution and adaptation. I yearn to understand the complex interactions within these ecosystems, the delicate balance that sustains them, and the impact of human activities on this delicate web. Understanding these complexities is crucial for our responsible stewardship of the planet and the preservation of its irreplaceable biodiversity. Furthermore, I am drawn to the intricacies of human history and its impact on our present reality. From the rise and fall of civilizations to the struggles for freedom and equality, historyoffers a lens through which we can examine the triumphs and failures of humankind.I crave a deeper understanding of the forces that have shaped our social,political, and economic systems, the ideologies that have fueled conflicts and cooperation, and the enduring legacies of past events. By studying history, wecan learn from our ancestors' mistakes and successes, equipping ourselves to navigate the challenges of the present and build a better future. The ever-evolving world of technology, with its rapid advancements in artificial intelligence, biotechnology, and space exploration, also holds a powerful allure.I am driven to understand the principles behind these innovations, their potential to address global challenges, and the ethical implications that accompany them. How can we harness the power of artificial intelligence for the betterment of society while mitigating potential risks? What are the ethical considerations surrounding genetic engineering and its impact on future generations? How can space exploration contribute to scientific advancements and inspire future generations? Exploring these frontiers of technology is essential for shaping a future where innovation serves humanity and the planet. Finally, I yearn to understand the very essence of creativity and its power to inspire, challenge, and transform. From the evocative brushstrokes of a painter to the soaring melodiesof a composer, creativity speaks a universal language that transcends cultural boundaries. What are the cognitive processes that underpin artistic expression? How does creativity foster innovation and problem-solving across disciplines? How can we nurture and cultivate our own creative potential to contribute to the world in meaningful ways? Understanding the nature of creativity is key to unlockingour own potential and enriching the human experience. In conclusion, the pursuit of knowledge is a lifelong journey, an insatiable thirst for understanding that fuels my curiosity and motivates my exploration. From the inner workings of the human mind to the vast expanses of the cosmos, from the intricate web of life on Earth to the enduring legacies of human history, from the frontiers of technology to the power of creative expression - these are the areas I yearn to understand with greater depth and clarity. This quest for knowledge is not merely an academic pursuit but a fundamental aspect of what makes us human - the desire to learn, grow, and contribute to the betterment of ourselves and the world around us.。
memory
Journal of Information Science OnlineFirst, published on April 10, 2007 as doi:10.1177/0165551506076331We propose a new index structure called F-Tree to improve the performance of index operations in a flash memory database system. We present the notion of compressed index rewriting to han-dle slow write/erase operations.data access due to system faults such as power failure. The well known index structure for a memory-based system is T-Tree [8]. T-Tree has the characteristics of the AVL-Tree, which has O(logN) tree tra-verse. T-Tree also has the characteristics of B-Tree, which has many entries in a node for space efficiency. T-Tree is considered a good structure for a memory-based index in terms of operation per-formance and space efficiency [10]. However, in [11], the performance of the B-Tree could outperform that of the T-Tree due to the concurrency control overhead. Thus, for performance reasons, a mem-ory-based system generally uses enhanced B-Trees as well as T-Trees.In disk-based systems, each node block may contain a large number of keys. The number of sub-trees in each node, then, may also be large. The B-Tree is designed to branch out in this large num-ber of directions and to contain a lot of keys in each node so that the height of the tree is relatively small. This means that only a small number of nodes must be read from the disk to retrieve an item. The goal is to have quick access to the data, and this means reading a very small number of records [12]. That is, the depth of the tree is very important because the number of node access refers to the number of disk I/O in the course of the tree traverse to search for or insert a node. Thus, the disk-based system minimizes the disk I/O cost by using a shallow and broad tree index. The memory-based system, on the other hand, does not prefer a shallow and broad tree index. This is because the depth of the tree or the size of the tree node can be adjusted to improve index performance, and the node access cost is very low in fast RAM.Unfortunately, index design and the implementation of a disk-based or memory-based system could not be applied directly to a flash memory-based system. This is due to the following draw-backs of flash memory. First, flash memory cannot be overwritten unless it is erased first. Note that writes and erases over flash memory take 20 and 200 times more than reads, respectively [13]. Thus, the write and erase operations should be handled differently from the read operation in terms of the execution cost. Second, the frequent erasing of some particular locations of the flash memory could quickly deteriorate the overall lifetime of the flash memory because each erasable unit has a limited cycle count on the erase operation. This requires the index management module to wear down all memory blocks as evenly as possible. Third, the write operation consumes nine times more energy than the read operation in portable devices, which have small batteries [10]. For these reasons, any direct application of the traditional index implementation to flash memory could result in severe performance degradation, and could significantly reduce its reliability.In order to improve the index operation performance of B-Tree in flash memory storage devices, B-Tree Flash Translation Layer (BFTL) [14] was proposed. BFTL could efficiently handle fine-grained updates caused by B-Tree index access and reduce the number of redundant write opera-tions in flash memory. The implementation of BFTL was done directly over the flash translation layer (FTL) so that no modifications to the existing application systems were needed. BFTL sits between the application layer and the flash memory block-device emulated by FTL.As shown in Figure 2, BFTL consists of a small reservation buffer and a node translation table. When the applications insert or delete records, the newly generated records are temporarily held by the reservation buffer to reduce redundant writes. Since the reservation buffer only holds an ade-quate amount of records, the index unit of the records should be timely committed (flushed) to the flash memory. The node translation table maintains the logical sector addresses of the index units of the B-Tree node so that the collection of the index units can be more efficient by smartly packing them into few sectors. Although BFTL achieves an enhanced performance in terms of write opera-tions, it requires an additional hardware implementation of the reservation buffer and the node translation table. Furthermore, the search overhead can increase with frequent access to the reser-vation buffer and the node translation table.Both BFTL and our scheme aim to improve index operation performances for flash memory stor-age devices. However, the focus of our scheme is different from that of BFTL, which aims to mini-mize the number of redundant write operations in B-Tree. In other words, our scheme aims to reuse tree nodes by index compression and node rewriting techniques in B+-Tree, which is an enhanced index of B-Tree. Our scheme sits between BFTL and FTL because it can compress and rewrite the packed index unit of BFTL. Therefore, our scheme and BFTL can be merged to maximize perform-ance synergy in flash memory storage systems.B-Tree-Related Applications Other ApplicationsNode TranslationTableThe CommitBFILPolicyReservation BufferFlash Memory Translation Layer (FIL)Flash Memory+-Tree.nodes within some pre-defined range. As data is inserted or removed from the data structure, the number of child nodes varies within a node, and so the internal nodes are coalesced or split so as to maintain the designed range. Because a range of child nodes is permitted, B+-Trees do not need re-balancing as frequently as other self-balancing binary search trees, but may waste some space since the nodes are not entirely full. The lower and upper bounds on the number of child nodes are typically fixed for a particular implementation [15]. The B+-Tree is kept balanced by requiring that all leaf nodes have the same depth. This depth will increase slowly as elements are added to the tree, but an increase in the overall depth is infrequent, resulting in all leaf nodes being one more hop further removed from the root. By maximizing the number of child nodes within each internal node, the height of the tree decreases, balancing occurs less often, and efficiency increases. In [8], the simulation shows that the average fill factor of 69% is the optimal value in a B+-Tree node. That is, saving too many entries in a node could lead to performance degradation due to frequent insert/delete operations caused by tree re-balancing.However, direct B+-Tree implementation on flash memory could lead to performance degradation due to slow write operations. Furthermore, this implementation could lead to system unreliability due to frequent write operations in the same location [5, 16]. Therefore, in order to implement the B+-Tree in flash memory, one of the most important goals is to reduce the number of write and erase operations. To achieve this goal, we propose a compressed index rewriting scheme that exploits sep-arated field compression and one-way rewrite techniques.As mentioned above, flash memory should perform erase operations prior to actual write opera-tions. Since erase operations set every bit to 1 to initialize each block, write operations are allowed to change individual bits from 1 to 0 in a one-way fashion. That is, in order to change from 0 to 1, another erase operation should be performed prior to the actual change. However, in the case of changing a cer-tain value from 1 to 0, flash memory could perform the rewrite operation without the erase operation [17]. If we exploit this special feature of a one-way rewrite, we could reduce the overhead of the write operation and enhance both the indexing performance and the lifetime of flash memory.The one-way rewriting technique is able to write the same node at least twice. This is achieved by compressing the original node prior to the first write operation. We achieved a 52% compression ratio by using reordered field compression. Since the compressed data area is sequentially allocated one by one, the rest of the 52% area of the original node can be reserved for another write opera-tion. If the second write operation is requested, the first half of the node is invalidated and the com-pressed data of the second one is sequentially allocated.Unfortunately, if the size of the contents obtained from the compression is larger than half of the original size, we do not exploit the one-way rewriting technique. However, we can avoid most of these cases by properly handling the level of compression algorithm. We propose an enhanced B+-Tree index called F-Tree, which exploits one-way rewriting and a reordered field compression. The internal and leaf node structures of the F-Tree index are illustrated in Figure 4.We used the LZO [18] algorithm for the node compression because LZO is not only simple but also easy for handling source codes. As shown in Table 2 [19], the LZO compression speed is about 4 MB/s and the decompression speed is about 15 MB/s in a Pentium 133MHz CPU. We can control the compression ratio and speed by varying the compression level of the LZO algorithm.We used the LZO-3level in Table 2 and slightly enhanced the node compression ratio by regroup-ing the key and pointer fields in a similar sequence. Our simulation showed that the compression ratio is 55.6% and 54.7% for the internal node and the leaf node, respectively. These compression ratios enable the one-way rewriting module to rewrite the original node after the first write opera-tion in the compression mode. If F-Tree exploits a more efficient compression algorithm than LZO, the index operation performance can be much more enhanced.Since CPU and RAM are far faster than flash memory access, the compression and decompres-sion overhead is negligible in our experiments. This is because the main performance factor is at a slow erase/write operation speed in flash memory, not at a read speed in CPU and RAM. In F-Tree, the main reason for node compression is to reduce slow erase/write operations by a one-way rewrit-ing procedure, and to contain more entries (keys and pointers) in the node. For I/O performance in F-Tree, the physical page size of flash memory corresponds to the size of one node. That is, one pageIn order to compress the leaf or internal nodes efficiently, FM_Write() separates the node entries into a key array and a pointer array and then calculates the key offset and the pointer offset, after which it compresses the array of differences between the original value and the offset. If the size of the contents obtained from the difference compression is smaller than half of the original size, it writes the compressed contents to the first half of the original node and reserves the second half, which is set to ‘1’, for the second write operation. If the second write operation is requested the next time, FTM invalidates the first half of the compressed node and rewrites the compressed contents in the second half.The detailed procedure of handling index operations can be shown in a pseudo-code form as Algorithm 1.Algorithm 1. Handling index operations for flash memoryint FM-Write(Node *N) // write data in flash memory and return compress mode{switch (Type(N)) {// check node typecase ROOT_NODE: // root node is not compressed.N. isCompressed←false;flash_memory_erase_operation(N);flash_memory_write_operation(N);return NOT_COMPRESSED;case LEAF_NODE: // leaf nodes or internal nodes can be compressed.case INTENAL_NODE:break;} // end of switch// compress key and pointerCreate a temp node T in RAM;key_offset←Minimum(N.K[1..n]); // find key offsetpointer_offset←Minimum(N.P[1..n]); // find pointer offsetT←LZO_compress_key(N, key_offset); // compress (Ki – key_offset)arrayT←T+ LZO_compress_pointer(N, pointer_offset);// compress (Pi – pointer_offset)arrayif ( SizeOf(T) >⎡SizeOf(N)/2⎤) {// check compressed size.N. isCompressed←false; // write non-compressed dataflash_memory_erase_operation(N);flash_memory_write_operation(N);return NOT_COMPRESSED;}// write compressed dataif ((first half is free) or (second half is full) ){// do first write: compress and write first half.N←T+ fill_second_half(1);// fill second half with 1 to rewrite second halfN. isCompressed←true;flash_memory_erase_operation(N);flash_memory_write_operation(N);return COMPRESSED_FIRST;}else {// second half is free//do second_write: compress and rewrite second half.N←fisrt_half+ T; // rewrite second halfN. isCompressed←true;flash_memory_write_operation(N); // just rewrite without erase operation.return COMPRESSED_SECOND;}} // End of FunctionNode *FM-Read(Node *N) // read data in flash memory{N←flash_memory_read_operation();switch (Type(N)) {// check node typecase ROOT_NODE: // root node is not compressed.return N;case LEAF_NODE: // leaf nodes or internal nodes may be compressed.case INTENAL_NODE:if ( N.isCompressed= false)return N;break;default:return NULL;// read error.} // end of switch.// uncompress entry(Ki ,Pi ).Create a temp node T in RAM;T.K[1..n]←LZO_uncompress_key(N); // uncompress key arrayT.P[1..n]←LZO_uncompress_pointer(N);// uncompress pointer array return T;} // End of FunctionNode*FT-Search (int key){R←block containing root node of tree;N←FM-Read(R); // read R in flash memorywhile ( Type(N) ¹ LEAF_NODE) { // N is not a leaf node of tree.q←number of tree pointers in node N ;if ( key≤N.K1) // N.Kirefers to the i th search field value in node N.N←N.P1 // N.Pirefers to the i th tree pointer in node N.// N is first child node.else if ( key> N.Kq-1)N←N.Pq // N is last child node.else {Search node N for an entry i such that N.Ki-1 <key≤N.Ki;N←N.Pi}N←FM-Read(N); }// end of while loopSearch node N for entry (Ki ,Pi) with key= Ki; // search leaf node N.if( entry_found ){N←N.Pireturn FM-Read(N) // read data record.}else {print_error ( “record with search field value key is not in the data file” );return NULL;}} // End of FunctionBoolean FT-Insert (int key, record*rec){R←block containing root node of tree; N←FM-Read(R);while ( Type(N) ≠LEAF_NODE) { // N is not a leaf node of tree.q←number of tree pointers in node N ;if ( key≤N.K1) N←N.P1else if ( key> N.Kq-1) N←N.Pqelse { Search node N for an entry i such that N.Ki-1 <key≤N.Ki; N←N.Pi}N←FM-Read(N); }// end of while loopSearch node N for entry(Ki ,Pi) with key= Ki; // search leaf node N.if(entry_found)return false; // record already in file, cannot insert.else {// insert entry in the tree to point to record.Create entry(K,Pr)where Pr points to the new record rec;if ( leaf node N is not full) {insert entry(K,Pr)in correct position in leaf node N;FM-Write(N);}else { // split if leaf node is fullCopy N to Temp // Temp is an oversize leaf node to hold extra entry.Insert entry(K,Pr)in Temp in correct position; // Temp now holds (Pleaf + 1) entries in RAM.New←Create a new empty leaf node for the tree;New.Pnext ←N.Pnext;J←é (Pleaf + 1)/2ù;N? First J entries in Temp;N.Pnext ←New;New←Remaining entries in Temp;Insert the leaf node New and key in correct position in parent internal node;FM-Write(N);FM-Write(New);// if parent is full, split it and propagate the split further up.}return true;} // end of else} // End of FunctionBoolean FM-Delete (int key){R←block containing root node of tree; N←FM-Read(R);while ( Type(N) ≠LEAF_NODE) { // N is not a leaf node of tree.q←number of tree pointers in node N ;if ( key≤N.K1) N←N.P1else if ( key>N.Kq-1) N←N.Pqelse { Search node N for an entry i such that N.Ki-1 <key≤N.Ki; N←N.Pi}N←FM-Read(N); }// end of while loopSearch node N for entry(Ki ,Pi) with key= Ki; // search leaf node N.if(! entry_found)return false; // key is not in tree, cannot delete. else {// delete entry and record.Remove entry (Ki ,Pi) in node N;FM-Write(N);Remove Record which Pi points to;FM-Write(Record);Decrease number of tree pointers in node N ;// underflow: if number of tree pointers is less than predefined minimum factor.if ( ! is_underflow(N) )return true;// handle underflow.if (Type(N) = ROOT_NODE) {Collapse N ;Make the remaining child the new root ; // so tree height decreases.FM-Write(N);}else { // merge immediate neighbor nodesL←number of entries in left node ;R←number of entries in right node ;if ( L> minimum_size&& R > minimum_size ) // minimum number of entries in a node return true;else { //either L or R is less than minimum_size in nodeBalance current node: // since the node has too few entries due to the removalInsert all the key values in the two nodes into a single node S;FM-Write(S);Merge with the neighbor whose pointer is the current node’s parent;FM-Write(N);// Continue merging the internal nodes until you reach a node with the correct size or the root node;}} // end of mergereturn true;} // end of else} // End of Function4.Performance evaluationWe compared the performance of F-Tree to the well known index scheme, B+-Tree, by means of com-puter simulation. Alternative approaches might include: (1) measurements from an actual imple-mentation, or (2) the use of approximate analytic methods. With regard to the first approach, even assuming that the obstacle resulting from a large programming effort to implement the whole usable system could be surmounted, there remains a problem with consistent measurement. This is because there are several interference factors such as kernel interrupt handling, context switching, paging, cleaning, garbage collecting, caching, prefetching, and buffering. These factors lead to unpredictable time delays and incorrect results in a real system.If an accurate and robust model is constructed in the analytic approach, it is much easier to eval-uate the performance in comparison with a simulation approach in terms of the required program-ming endeavour and the computing resources. However, constructing an accurate analytic model is not easy because it possesses a complex and dynamic run time nature, which is hard to model with clear mathematical expressions. Although all the detailed factors for the run time nature are included in the analytical model, usually a simulation study or an actual measurement needs to be performed in order to validate the performance evaluation results of an analytic model.4.1.Simulation setupWe compared the performance of the F-Tree index with two types of the B+-Tree index. The average fill factor of the F-Tree was fixed at 69%, which is a general value for the best performance [8]. We denote this F-Tree as FTR69. On the other hand, we divided the average fill factor of B+-Tree intoQueuing system model for simulation.[3]K. Yim, A novel memory hierarchy for flash memory based storage systems, Journal of SemiconductorTechnology and Science5(4) (2005) 262–9.[4] C. Park, J. Seo, D. Seo, S. Kim and B. Kim, Cost-efficient memory architecture design of NAND flash mem-ory embedded systems. In: Proceedings of the21st International Conference on Computer Design, San Jose, California,13–15 October 2004(IEEE Computer Society, 2004) 474–9.[5] C. Wu, L. Chang and T. Kuo, An efficient R-Tree implementation over flash-memory storage systems. In:E. Hoel and P. Rigaux (eds),Proceedings of the ACM GIS03, New Orleans, Louisiana, 2003(ACM, NewYork, 2003)17–24.[6]H. Kim and S. Lee, A new flash memory management for flash storage system. In:Proceedings of the 23rdAnnual International Computer Software and Applications Conference,Phoenix, Arizona, 1999(IEEE Computer Society, 1999) 284–9.[7]S.K. Cha, J.H. Park, and B.D. Park, Xmas: an extensible main-memory storage system. In: F. Golshani andK. Makki (eds), Proceedings of the 6th ACM International Conference on Information and Knowledge Management,10–14 November 1997(ACM, New York, 1997) 356–62.[8]R. Elmasri and S.B. Navathe, Fundamentals of Database Systems(Addison-Wesley, 1994).[9]N. Beckmann, H.P. Kriegel, R. Schneider and B. Seeger, The R*Tree: an efficient and robust access methodfor points and rectangles. In: H. Garcia-Molina and H.V. Jagadish (eds), Proceedings of the ACM SIGMOD International Symposium on the Management of Data, 23– 25May 1990(ACM, New York, 1990) 322–31.[10] C. Lee, K. Ahn and B. Hong, A study of performance decision factor for moving object database in mainmemory index. In: Y. Eom (ed.)Proceedings of the Korea Information Processing Society 2003 Spring Conference, Seoul10–11 May(KIPS, Seoul, 2003) 1575– 8.[11]H. Lu, Y. Yeung Ng and Z. Tang, T-Tree or B-Tree: main memory database index structure revisited. In:Proceedings of the 11th Australasian Database Conference,31 January–3 February 2000(IEEE Computer Society, 2000).[12]Software Design Using C++(2006). Available at: /carlsond/swdesign/btree/btree.html (accessed 5 February 2006).[13]What is Flash?(2006) Available at: /Products/Semiconductor/Flash/WhatisFlash/FlashStructure.htm (accessed 11 July 2006).[14] C. Wu, L. Chang and T. Kuo, An efficient B-Tree layer for flash-memory storage systems.In: J Chen andS.S. Hong (eds), Proceedings of the RTCSA, Tainan, Taiwan, 18–20 February 2003(Springer, Berlin/ Heidelberg, 2003) 409–30. [Springer, Lecture Notes in Computer Science, 2968/2004.][15]B-tree (2006). Available at: /wiki/B-tree (accessed 5 February 2006).[16]J. Nam and D. Park, The efficient design and implementation of the B-Tree on flash memory. In: M.H. Kim(ed.)Proceedings of the 32nd Korea Information Science Society Fall Conference, Seoul, 11–12 November, 2005(KISS, Seoul, 2005) 55–7.[17]J. Jeong, S. Noh, S. Min and Y. Cho, A design and implementation of flash memory simulator, Journal ofKorean Information Science: C 8(1) (2002) 36–45.[18]LZO (2006). Available at: /opensource/lzo/#download (accessed 5 February 2006).[19]LZO (2006). Available at: /opensource/lzo/lzodoc.php (accessed 5 February 2007)[20]H. Schwetman, CSIM User’s Guide for Use with CSIM Revision 16(Microelectronics and ComputerTechnology Corporation, Austin, 1992).[21]K. Yim and K. Koh, A study on flash memory based storage systems depending on design techniques. In:Proceedings of the 30th Korea Information Science Society Fall Conference, Seoul, 21–22 October, 2003 (KISS, Seoul, 2003) 274–6.。
MEMORY COMPRESSION
专利名称：MEMORY COMPRESSION 发明人：DENT, PAUL, WILKINSON 申请号：EP2005005994申请日：20050603公开号：WO2005119920A3公开日：20060216专利内容由知识产权出版社提供摘要：Exemplary embodiments of the present invention comprise a method for compressing data for storage in memory. According to one embodiment, the method forms a set of values based on a monotonically ordered series of look-up table values. For one or more paired values in the set, the exemplary method generates a difference of the values in the pair. After replacing one of the values in the pair with a value based on the differences to modify the set, the exemplary method stores final values based on the modified set in memory. The present invention also comprises memory for storing the look-up table values. One exemplary memory comprises a decoder, an encoder, and one or more patterns of crisscrossed interconnect lines that interconnect the encoder with the decoder. The patterns of crisscrossed interconnection lines may be implemented on one or more planar layers of conductor tracks vertically interleaved with isolating material.申请人：TELEFONAKTIEBOLAGET LM ERICSSON (PUBL),DENT, PAUL, WILKINSON 更多信息请下载全文后查看。
记忆是一颗种子英语作文
记忆是一颗种子英语作文Memory: A Seed of Growth.In the tapestry of our lives, memories are the threads that weave together the fabric of our experiences, shaping who we are and guiding our path forward. Like seeds planted in fertile soil, memories have the potential to blossominto knowledge, wisdom, and profound personal transformation.The Genesis of Memory.Memories are born from the interactions we have with the world around us. Every moment, our brains are bombarded with sensory input that is processed and stored in various neural networks. This process, known as memory consolidation, allows us to encode and retrieve information in order to make sense of our experiences and navigate our environment.The hippocampus, a seahorse-shaped structure deepwithin the brain, plays a crucial role in memory formation. It acts as a temporary storage facility where new memories are initially encoded. Over time, these memories are transferred to the neocortex, where they are stored more permanently.Types of Memory.Memories can be broadly classified into two main types: explicit and implicit. Explicit memories are those that we can consciously recall and articulate, such as facts, events, and personal experiences. Implicit memories, on the other hand, are automatic and unconscious, influencing our behavior and preferences without our conscious awareness.Explicit memories can be further subdivided into episodic and semantic memories. Episodic memories are personal experiences that are associated with a specific time and place, such as remembering your first day of school or a memorable family vacation. Semantic memories,in contrast, are facts and general knowledge that arestored as concepts and ideas, such as the capital of France or the laws of physics.Implicit memories include procedural memories, which involve learned motor skills and habits, such as riding a bike or playing a musical instrument. Priming, a form of implicit memory, occurs when exposure to a stimulus makes us more likely to respond to related stimuli. For example, if you hear the word "dog," you may be more likely to notice a dog in a crowd.The Dynamics of Memory.Memory is not a static entity. Instead, it is a dynamic and ever-changing process. Over time, memories can fade, become distorted, or even be reconstructed. This process is influenced by a variety of factors, including the strength of the original memory, the number of times it is accessed, and our emotional state.The act of recalling a memory can also alter it. Each time a memory is retrieved, it is reconstructed based onthe current context and our existing beliefs. This can lead to distortions and inaccuracies, especially over long periods of time.The Importance of Memory.Memory is essential for our survival and well-being. It allows us to learn from our past experiences, adapt to changing circumstances, and make informed decisions. Without memory, we would be stranded in the present, unable to navigate the complexities of life.On a personal level, memories connect us to our past and shape our identity. They provide us with a sense of continuity and belonging, allowing us to reflect on our accomplishments and learn from our mistakes. Memories also fuel our creativity and imagination, inspiring us to create new experiences and dream of a better future.Cultivating a Healthy Memory.Like any aspect of our health, memory can be nurturedand enhanced. There are a number of evidence-based strategies we can employ to improve our memory function:Regular Exercise: Exercise has been shown to increase blood flow to the brain, which promotes memory consolidation. Aim for at least 150 minutes of moderate-intensity exercise each week.Mind-Stimulating Activities: Engage in mentally challenging activities, such as reading, puzzles, or learning a new language. These activities help keep your mind sharp and improve cognitive function.Sleep: Sleep is essential for memory consolidation. Aim for 7-9 hours of quality sleep each night.Nutrition: A healthy diet that includes fruits, vegetables, whole grains, and lean protein can nourish your brain and support memory function.Stress Management: Chronic stress can impair memory. Find healthy ways to manage stress, such as exercise, yoga,or meditation.Memory Techniques: There are a number of memory techniques that can help you improve your recall, such as active recall, spaced repetition, and mnemonics.Conclusion.Memory is a precious gift that allows us to connect with our past, navigate the present, and envision the future. By understanding the nature of memory and cultivating healthy habits, we can nurture this vital capacity and unlock its full potential for personal growth and transformation.Like a seed planted in the fertile soil of our minds, memories hold the promise of wisdom, knowledge, and the fulfillment of our life's purpose. By tending to the soil of our memory, we can ensure that these seeds flourish and bear abundant fruit.。
memory
Abstract
1
Introduction
This paper describes work being carried out in a project to develop compilers from data-parallel (DP) languages targeted to distributed-memory multiprocessors. In particular it introduces the intermediate language F-code and techniques developed in its compilation. F-code is a high-level, syntax-free representation of data-parallel programs. We call this a portable software platform as it serves as a platform on which language-independent and target-independent compiler research may be undertaken. F-Code was a logical development of a user-level language EVAL [5] which introduced many novel features into data-parallel languages. F-code generalises the rank and shape coercion mechanisms of EVAL, allows complex lhs expressions and uses the same sub-typing structure as used in EVAL. Moreover it provides storage models to support compilation from virtually any DP language, whether it be based on FORTRAN, Pascal, C or indeed any other imperative language. The only language which matches, in expressiveness, the kind of dataparallelism found in F-code is APL and APL2 [4], indeed F-code is often more orthogonal in its expressivity. The only area in which APL2 is more expressive is in the notion of recursive array objects as F-code manipulates strictly at data objects (i.e. a parallel data object has scalar type). F-code is an imperative language which reconciles assignment with referential transparency by allowing as much asynchrony as possible in the evaluation of imperative programs. Compilers may therefore exploit the functional parallelism found within DP expressions, while remaining conservative only where required in order to retain coherency between dependent assignments. We believe that there is just one other system that reconciles assignment with referential transparency. In [1] an extension is given to -calculus [2] to represent additional constructs and reduction rules for mutable variables and assignments. This new calculus does have the Church-Rosser property. In other words, it is possible for a functional language to include assignment: a language can naturally express advanced imperative constructs without destroying the algebraic properties of the functional subset. The functional parts (environments) in Fcode which are the data-parallel expressions possess referential transparency and may be executed with all the asynchrony of a functional program. That includes lazy evaluation. Besides the fact that F-Code is data-parallel, there is one important distinction to be made between F-Code and -calculus (augmented, or not, with assignment): the cost model. The programmer's cognizance of execution cost for a real functional program (based on -calculus, or not) is minimal; and there exist semantic-preserving transformations that can dramatically change that cost [6]. However, some semblance of the execution cost of an F-Code program can automatically be inferred from the program's algorithm. This requires the aid of run-time statistics and/or the programmer's intuitive knowledge. This knowledge is represented in a type-system. Thus, unlike using functional programming languages, the programmer knows how to write an ecient program, portably, even while it is suitably high-level, and abstract.
Types of Memory
Declarative Memory
Declarative memory: stores facts or events. There are two kinds of declarative memory.
Sensory Memory
Iconic Memory: a form of sensory memory that automatically hold visual information for about quarter of a second or more. When shifting your attention the information disappears.
Therapist’s role in recovered memories: they use images, hypnosis, true serum, sodium amatol.
Do Emotions Affect Memories?
Emotions seem to “stamp-in” memories, because if something happens during a strong emotion the chances of remembering a situation, person, or event are greater.
Short Term Memory: Working
Sensory Memory: a process of receiving and holding information, in its raw state, for a short period of time (from an instant to several seconds).
memory training
或段落 ，尝试建立记忆支点 和记忆线。然后用中文复述所听得内容。 听辨中 不作记录。 One –Sentences (07:08—08:30) 记忆技 能训练
Task
Task
Task
Two– Paragraphs (33:20-34:40) Three– Passage （01:03:50—05:18）
Short-term
memory is constantly utilized by interpreters and note-taking is to assist STM.
SHORT-TERM MEMORY TRAINING 记忆点、记忆线、记忆网
Analyze
the logic of the text 1. classification 2. cause-effect 3. compare-contrast 4. sequencing 5. simple listing
Ceremonial speeches
Flowering words: 1. Distinguished guests , ladies and gentlemen.
2. I have the pleasure/honor/privilege to declare the 11th Asian Games open.
Thank you for your gracious hospitality.
5.
LISTEN TO THE SPEECH SEGMENT BY SEGMENT AND INTERPRET THEM INTO CHINESE AT THE END OF EACH SEGMENT. .
•Refer to page 31 of 《高口》 (03:00)
记忆过程作文英语模板
记忆过程作文英语模板英文回答：The Process of Memory。
Memory is the ability to encode, store, and retrieve information. It is a complex process that involves multiple brain regions and functions.Encoding。
The first step in memory is encoding, which is the process of converting sensory information into a form that can be stored in the brain. Encoding can be either automatic or effortful. Automatic encoding occurs without conscious effort, such as when we remember faces or names. Effortful encoding requires conscious effort, such as when we memorize a list of items.Storage。
Once information has been encoded, it is stored in the brain. There are two main types of memory storage: short-term memory and long-term memory. Short-term memory stores information for a few seconds or minutes, while long-term memory stores information for an indefinite period of time.Retrieval。
energetic记忆方法
Energetic Memory TechniquesIntroductionMemory is an essential cognitive function that allows us to store, retain, and recall information. However, many people struggle with remembering things, whether it’s a simple grocery list or important facts for an exam. Luckily, there are various memory techniquesavailable to enhance our ability to remember. One such technique is the “Energetic Memory Method.”What is the Energetic Memory Method?The Energetic Memory Method is a memory enhancement technique that focuses on using physical movements and energy to improve memory retention and recall. This method is based on the principle that engaging the body and mind together can enhance memory performance.How does it work?The Energetic Memory Method involves three main steps: preparation, encoding, and recall.1. PreparationBefore using the Energetic Memory Method, it is essential to create an optimal learning environment. Find a quiet and comfortable space where you can focus without distractions. Prepare the materials you need, such as textbooks, notes, or flashcards.2. EncodingEncoding is the process of transforming information into a format that can be stored and later retrieved. In the Energetic Memory Method, encoding involves associating the information you want to remember with physical movements and energy.a. VisualizationStart by visualizing the information you want to remember. Create mental images that represent the concepts or facts you are trying to encode. The more vivid and detailed the images, the better.b. Physical movementsNext, associate each piece of information with a specific physical movement. For example, if you are trying to remember a list of countries, you can assign a unique hand gesture or body movement to each country. Repeat the movements while visualizing the corresponding information.c. Energy and emotionInfuse the encoded information with energy and emotion. Attach a feeling or emotion to each piece of information to make it more memorable. For example, if you are studying historical events, imagine the excitementor sadness associated with those events.3. RecallThe final step of the Energetic Memory Method is recall. This is the process of retrieving the encoded information from your memory. Torecall the information effectively, follow these steps:a. Replicate the physical movementsRecreate the physical movements associated with the information you want to remember. By mimicking the movements, you activate the muscle memory, which can trigger the associated information.b. Visualize the encoded imagesVisualize the mental images you created during the encoding phase. Tryto recreate the vividness and details of the images. This visualization helps retrieve the encoded information from your memory.c. Recall the associated emotionsRecall the emotions or feelings you associated with the information. By re-experiencing the emotions, you can trigger the associated memories.Benefits of the Energetic Memory MethodThe Energetic Memory Method offers several benefits:1. Enhanced memory retentionBy engaging both the mind and body, the Energetic Memory Method improves memory retention. The physical movements and energy associated with the information create stronger neural connections, making it easier torecall the information later.2. Increased focus and attentionThe Energetic Memory Method requires active engagement with the material, leading to increased focus and attention. By incorporating physical movements and energy, you are less likely to become bored or distracted during the learning process.3. Improved long-term memoryThe combination of visualization, physical movements, and emotions strengthens the encoding process, leading to improved long-term memory. The memories formed using this method are more likely to be retained and recalled accurately over an extended period.4. VersatilityThe Energetic Memory Method can be applied to various types of information, including facts, lists, concepts, and even complex theories. It is a versatile technique that can be adapted to suit individual learning styles and preferences.ConclusionThe Energetic Memory Method is a powerful technique that harnesses the connection between the mind and body to enhance memory performance. By incorporating physical movements, visualization, and emotions, this method improves memory retention and recall. With practice and consistency, anyone can benefit from this technique and improve their ability to remember information effectively. So why not give it a tryand unlock your full memory potential?。
renowned记忆方法
renowned记忆方法Renowned Memory MethodIntroduction:Memory plays a crucial role in our daily lives. Whether it's remembering important information for exams or recalling details from a conversation, having a good memory can greatly enhance our productivity and efficiency. However, many people struggle with memory retention and often find it challenging to remember things. This is where the renowned memory method comes into play. In this article, we will explore this effective memory technique and understand how it can help improve our memory capacity.What is the Renowned Memory Method?The renowned memory method is a proven technique that leverages visualization and association to enhance memory recall. It is based on the principle that our brains have a natural inclination towards retaining visual information more effectively than abstract concepts. By utilizing this inherent capability, the renowned memory method enables individuals to remember complex information with relative ease.How Does it Work?The renowned memory method involves three main steps: encoding, storing, and retrieving.1. Encoding:The first step in this method is to encode the information you want to remember into vivid and memorable images. Let's take an example of learning a new language. Instead of trying to memorize individual words and their translations, you can associate each word with a unique mental image. For instance, if you want to remember the word "apple" in French, you can visualize a juicy red apple.2. Storing:Once you have encoded the information, it is essential to store it in a structured manner. One effective way to do this is by creating a mental "memory palace." This technique involves associating each piece of information with a specific location in an imaginary building or a familiar place. As you mentally walk through this space, you can easily retrieve the stored information by recalling the associated images.3. Retrieving:The final step is to retrieve the information when needed. By mentally revisiting the memory palace and recalling the associated images, you can retrieve the stored information effortlessly. The more you practice this method, the stronger your memory will become, and the easier it will be to recall information on demand.Benefits of the Renowned Memory Method:The renowned memory method offers several benefits for individuals looking to improve their memory capacity:1. Enhanced Memory Retention: By leveraging visualization and association techniques, this method allows for more effective encoding and retrieval of information, leading to improved memory retention.2. Time Efficiency: With the renowned memory method, you can significantly reduce the time required to memorize and recall information. This technique enables you to remember complex details more quickly and efficiently.3. Versatility: The application of the renowned memory method is not limited to specific domains. Whether it's learning new languages, memorizing historical dates, orrecalling scientific concepts, this method can be applied to any subject matter.4. Long-Term Memory Improvement: Regular practice of the renowned memory method can lead to long-term memory improvement. As you continue to strengthen your memory capacity, you will find it easier to remember information for extended periods.Conclusion:Memory is a fundamental aspect of human cognition, and improving it can have a significant impact on various aspects of our lives. The renowned memory method offers a practical and effective way to enhance memory retention and recall. By leveraging visualization and association techniques, this method enables individuals to remember information more vividly and effortlessly. So, whether you are a student preparing for exams or a professional seeking to improve your knowledge retention, give the renowned memory method a try and experience the positive impact it can have on your memory capacity.。
何燕的外文翻译
微机系统在很多情况下，电子系统的用途是来处理信息的，这种被用于处理的信息可能是电话交谈，仪表示数的读取或者是一个公司的帐户，但是在每种情况下运行的相同类型主要涉及：信息处理，信息存储和信息的传输。
在传统的电子设计中，这些操作均以功能平台的方式组合起来，例如一个计数器，无论它是电子的计数器还是机械的计数器，都是需要将当前的数值存起来的，而且按照一定的要求将该数值加1。
一种系统，例如一个电子时钟的任何一个系统，它采用的计数器具有其存储和处理能力的散布在整个系统中，因为每个计数器都可以存储和处理部分数字。
现今，基于微处理器的系统从这种常规的处理方式里面分离开来，它把信息处理，信息的存储和信息传输这三种功能分离成不一样系统单元。
这种把系统划分成三部分的分离方法是冯·诺依曼于20世纪40年代设想并提出来的，这个设想是针对微型计算机的设想，自此以后，几乎所有制造的计算机都是以这种结构来设计的，尽管它们在物理形式和物理结构上的范围十分广泛，但是从根本上来说它们都是有着相同基本设计的计算机。
在基于微处理器的系统中，信息的处理将由以微处理器为基础的系统本身来执行完成。
存储是利用存储器电路的，然而系统中信息沟通的输入和输出将是由特殊的输入/输出（I/O）电路。
要在一个以微处理器为基础的时钟里面找出能够执行既有技术功能的特殊硬件的组成部分这将是不可能的，因为时间将被存储在存储器中，并在固定的时间间隔条件下由微处理器来控制增值。
然而，由于系统是几乎完全由软件来定义。
因此对于微处理器的结构和其辅助的电路这种看起来十分抽象的处理方式确使其在具体应用的时候非常灵活。
这样的设计过程主要是软件工程中的一个，并且在生产软件时，就会碰到在传统的生产工程中类似的构造和系统维护的问题。
图1.1 微型计算机的三大组成部分在图1.1说明了微型计算机内这三个部分在一个微处理器控制系统中是怎么样按照机器之中的信息通讯方式而链接起来的。
记忆计算概念、特性及研究进展
到稿日期：2009-03-20 返修日期：2009-06-20 本文受国家自然科学基金 (No. 61332005,61373119,61222209); 国家重点基础研究发展计划(973计划) (No. 2015CB352400)资助。
郭斌(1980—)，男，博士，教授，博士生导师，CCF 高级会员，主要研究方向为普适计算、移动互联网、群智感知等(通信作者)，E-mail: guob@ ；李文鹏(1993—)，男，硕士生，主要研究方向移动群智感知；陈荟慧(1978—)，女，博士生，主要研究方向为移动群智感知；於志文(1977—)，男，博士，教授，博士生导师，CCF 高级会员，主要研究方向为普适计算、移动互联网和智能信息技术；姜佳君(1992—)，男，硕士生；王文辉(1993—)，男，硕士生。
记忆计算：概念、特性及研究进展郭斌 李文鹏 陈荟慧 於志文 姜佳君 王文辉 （西北工业大学计算机学院， 西安 ７１０１２９）摘 要 随着信息技术的发展，尤其是移动互联网与物联网的发展，有关个人工作和生活的数据呈现指数型增长。
在这海量的数据中蕴含着丰富而有价值的个人信息，如何从这些数据中挖掘出有价值的信息成为当前信息领域的重要问题。
针对这个问题，文中介绍了普适计算领域新近兴起的研究主题——记忆计算。
记忆计算旨在通过各种带感知和计算功能的设备，比如智能手机、可穿戴设备等，实时感知和捕获用户线上线下活动的数据，分析并挖掘其内在价值，进而组织和管理有意义的记忆数据，实现基于情境的记忆数据呈现，以辅助个体记忆，支持社群交流与协作。
文中讨论了基于移动情境感知的记忆计算的概念、特性、系统模型以及当前研究的关键技术与挑战，综述了记忆计算在生活日志、记忆提醒、往事回忆和群体记忆分享等方面的研究进展，并对其未来发展进行了展望。
关键词 普适计算，记忆计算，情境感知，数字记忆，移动及可穿戴设备 中图法分类号 TP391 文献标识码 AMemory Computing: Concept, Characteristics and AdvancesGUO Bin LI Wen-Peng CHEN Hui-Hui YU Zhi-Wen JIANG Jia-Jun W ANG Wen-Hui(School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129, China)Abstract With the development of information and communication technology, especially the mobile Internet and the Internet of Things, the data about personal works and life has shown an exponential boom. Since many valuable individual information are invisibly contained in these high-volume dataset, how to extract the valuable information from these great amount of data has become a new challenge. This article introduces an emerging research topic in the pervasive computing field named memory computing. By utilizing prevalent sensor-embedded smartphones and wearable devices, memory computing collects heterogeneous online/offline data of user’s daily activities, extracts and mines inner memory data and associated contexts, manages useful data in the right manner, and renders context-aware individual memory information for helping users recall their memories and promoting their communication and cooperation. This paper presents the concept, features, and the system model of memory computing, and discusses its key technologies and research challenges. We also review the applications of memory computing, such as life logging, reminiscence, memory reminding, and community memory sharing, and prospect the future works.Keywords Pervasive computing ，Memory computing ，Mobile context awareness ，Digital memory ，Mobile and wearabl e devices1 前言记忆是一个复杂的认知行为，对人类的日常生活和工作非常重要。
相变随机存储器材料与结构设计最新进展
基金项目:国家重点基础研究发展计划(2007CB 935400,2006CB 302700);国家863计划资助项目(2006AA03Z360,2008AA031402);国家自然科学基金资助项目(60706024);上海市科委资助项目(06QA14060,06XD14025,0652n m003,06D Z22017,0752nm013,07QA14065)相变随机存储器材料与结构设计最新进展刘波,宋志棠,封松林(中国科学院上海微系统与信息技术研究所纳米技术研究室,上海200050)摘要:介绍了相变随机存储器(PCR AM )的研究现状,包括新型相变材料、热阻层材料和器件结构等。
分析了GeSb 和SiSb 等相变材料具有组分简单、数据保持力好、优良的存储性能等特点,介绍了PCRAM 这一新型半导体存储器的基本原理、特点以及国内外有关相变材料、过渡层材料和器件结构设计等方面的研究进展,最后提出了我国发展PCRAM 的几点思考。
关键词:相变随机存储器;相变材料;热阻层材料中图分类号:TN333.8 文献标识码:A 文章编号:1003-353X (2008)09-0737-06Recent Progress on the Materials and Structures for Phase -ChangeRandom Access MemoryLiu Bo ,Song Zhitang ,Feng Songlin(Labo ratory o f Nanotechnolo gy ,Shanghai Ins titute o f M icros ystem and Info rmatio n Technology ,Chinese Academy of Sciences ,Shanghai ,200050)A bstract :The current situation of phase -change random access memory (PCRAM )is r evie wed ,including new phase change materials ,buffer la yer materials ,and cell structures .With the advantages of simple composition ,good data retention ,and excellent memory per for mance ,GeSb and SiSb are both newphase -change materials with good application potential in PCR AM chips .The developing strategy and destination of PCRAM in China and abroad are proposed on ne w materials and device structures .And some considerations on the development of PCR AM in China are proposed .Key words :phase change random access memor y ;phase change material ;ther mal insulator layer materialEEAC C :1265D0 引言相变随机存储器(PCRAM )是基于Ovshinsky 在20世纪60年代末提出的奥弗辛斯基电子效应的存储器[1-2]。