Academic literature on the topic 'Post's embedding problem'
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Journal articles on the topic "Post's embedding problem"
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Nies, André, Richard A. Shore, and Theodore A. Slaman. "Definability in the Recursively Enumerable Degrees." Bulletin of Symbolic Logic 2, no. 4 (December 1996): 392–404. http://dx.doi.org/10.2307/421171.
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§1. Introduction. Natural sets that can be enumerated by a computable function (the recursively enumerable or r.e. sets) always seem to be either actually computable (recursive) or of the same complexity (with respect to Turing computability) as the Halting Problem, the complete r.e. set K. The obvious question, first posed in Post [1944] and since then called Post's Problem is then just whether there are r.e. sets which are neither computable nor complete, i.e., neither recursive nor of the same Turing degree as K?Let be the r.e. degrees, i.e., the r.e. sets modulo the equivalence relation of equicomputable with the partial order induced by Turing computability. This structure is a partial order (indeed, an uppersemilattice or usl)with least element 0, the degree (equivalence class) of the computable sets, and greatest element 1 or 0′, the degree of K. Post's problem then asks if there are any other elements of .The (positive) solution of Post's problem by Friedberg [1957] and Muchnik [1956] was followed by various algebraic or order theoretic results that were interpreted as saying that the structure was in some way well behaved:Theorem 1.1 (Embedding theorem; Muchnik [1958], Sacks [1963]). Every countable partial ordering or even uppersemilattice can be embedded into .Theorem 1.2 (Sacks Splitting Theorem [1963b]). For every nonrecursive r.e. degreeathere are r.e. degreesb, c < asuch thatb ∨ c = a.Theorem 1.3 (Sacks Density Theorem [1964]). For every pair of nonrecursive r.e. degreesa < bthere is an r.e. degreecsuch thata < c < b.
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Makarov, Ilya, Mikhail Makarov, and Dmitrii Kiselev. "Fusion of text and graph information for machine learning problems on networks." PeerJ Computer Science 7 (May 11, 2021): e526. http://dx.doi.org/10.7717/peerj-cs.526.
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Today, increased attention is drawn towards network representation learning, a technique that maps nodes of a network into vectors of a low-dimensional embedding space. A network embedding constructed this way aims to preserve nodes similarity and other specific network properties. Embedding vectors can later be used for downstream machine learning problems, such as node classification, link prediction and network visualization. Naturally, some networks have text information associated with them. For instance, in a citation network, each node is a scientific paper associated with its abstract or title; in a social network, all users may be viewed as nodes of a network and posts of each user as textual attributes. In this work, we explore how combining existing methods of text and network embeddings can increase accuracy for downstream tasks and propose modifications to popular architectures to better capture textual information in network embedding and fusion frameworks.
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Ambos-Spies, K., and M. Lerman. "Lattice embeddings into the recursively enumerable degrees." Journal of Symbolic Logic 51, no. 2 (June 1986): 257–72. http://dx.doi.org/10.1017/s0022481200031133.
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The classification of algebraic structures which can be embedded into ℛ, the uppersemilattice of recursively enumerable degrees, is the key to answering certain questions about Th(ℛ), the elementary theory of ℛ. In particular, these classification problems are important for answering decidability questions about fragments of Th(ℛ). Thus the solutions of Fried berg [F] and Mučnik [M] to Post's problem were easily extended to show that all finite partially ordered sets are embeddable into ℛ, and hence that ∃1 ∩ Th(ℛ), the existential theory of ℛ, is decidable. (The language used is ℒ′, the pure predicate calculus together with a binary relation symbol ≤ to be interpreted as the ordering of ℛ) The problem of determining which finite lattices are embeddable into ℛ has been a long-standing open problem, and is one of the major obstacles to determining whether ∀2 ∩ Th(ℛ), the universal-existential theory of ℛ, is decidable. Shore has obtained some nice partial results in this direction. Embeddings also played a central role in showing that Th(ℛ) is not ℵ0-categorical (Lerman, Shore and Soare [LeShSo]), thus resolving a problem posed by Jockusch. Harrington and Shelah [HS] embedded all 0′-presentable partially ordered sets into ℛ in such a way that the partially ordered sets can be uniformly recovered from four parameters. They used these embeddings to show that Th(ℛ) is undecidable.The first nontrivial extension of the embeddings of Friedberg and Mučnik to lattice embeddings was obtained independently by Lachlan [La1] and Yates [Y] who showed that the four-element Boolean algebra can be embedded into ℛ. Thomason [T] and Lerman independently extended this result to include all finite distributive lattices. The nondistributive case, however, was much more difficult. Lachlan [La2] embedded the two five-element nondistributive lattices M5 and N5 (see Figures 1 and 2) into ℛ, and his proof could easily have been extended to include a larger class of lattices.
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Chen, Jing, Jun Feng, Xia Sun, and Yang Liu. "Co-Training Semi-Supervised Deep Learning for Sentiment Classification of MOOC Forum Posts." Symmetry 12, no. 1 (December 18, 2019): 8. http://dx.doi.org/10.3390/sym12010008.
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Sentiment classification of forum posts of massive open online courses is essential for educators to make interventions and for instructors to improve learning performance. Lacking monitoring on learners’ sentiments may lead to high dropout rates of courses. Recently, deep learning has emerged as an outstanding machine learning technique for sentiment classification, which extracts complex features automatically with rich representation capabilities. However, deep neural networks always rely on a large amount of labeled data for supervised training. Constructing large-scale labeled training datasets for sentiment classification is very laborious and time consuming. To address this problem, this paper proposes a co-training, semi-supervised deep learning model for sentiment classification, leveraging limited labeled data and massive unlabeled data simultaneously to achieve performance comparable to those methods trained on massive labeled data. To satisfy the condition of two views of co-training, we encoded texts into vectors from views of word embedding and character-based embedding independently, considering words’ external and internal information. To promote the classification performance with limited data, we propose a double-check strategy sample selection method to select samples with high confidence to augment the training set iteratively. In addition, we propose a mixed loss function both considering the labeled data with asymmetric and unlabeled data. Our proposed method achieved a 89.73% average accuracy and an 93.55% average F1-score, about 2.77% and 3.2% higher than baseline methods. Experimental results demonstrate the effectiveness of the proposed model trained on limited labeled data, which performs much better than those trained on massive labeled data.
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Riza, M. Alfa, and Novrido Charibaldi. "Emotion Detection in Twitter Social Media Using Long Short-Term Memory (LSTM) and Fast Text." International Journal of Artificial Intelligence & Robotics (IJAIR) 3, no. 1 (May 31, 2021): 15–26. http://dx.doi.org/10.25139/ijair.v3i1.3827.
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Emotion detection is important in various fields such as education, business, employee recruitment. In this study, emotions will be detected with text that comes from Twitter because social media makes users tend to express emotions through text posts. One of the social media that has the highest user growth rate in Indonesia is Twitter. This study will use the LSTM method because this method is proven to be better than previous studies. Word embedding fast text will also be used in this study to improve Word2Vec and GloVe that cannot handle the problem of out of vocabulary (OOV). This research produces the best accuracy for each word embedding as follows, Word2Vec produces an accuracy of 73,15%, GloVe produces an accuracy of 60,10%, fast text produces an accuracy of 73,15%. The conclusion in this study is the best accuracy was obtained by Word2Vec and fast text. The fast text has the advantage of handling the problem of out of vocabulary (OOV), but in this study, it cannot improve the accuracy of word 2vec. This study has not been able to produce very good accuracy. This is because of the data used. In future works, to get even better results, it is expected to apply other deep learning methods, such as CNN, BiLSTM, etc. It is hoped that more data will be used in future studies.
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Tadesse, Michael Mesfin, Hongfei Lin, Bo Xu, and Liang Yang. "Detection of Suicide Ideation in Social Media Forums Using Deep Learning." Algorithms 13, no. 1 (December 24, 2019): 7. http://dx.doi.org/10.3390/a13010007.
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Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal posts. We address the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media. For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models. Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results. Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.
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Young, Ethan S., Allison K. Farrell, Elizabeth A. Carlson, Michelle M. Englund, Gregory E. Miller, Megan R. Gunnar, Glenn I. Roisman, and Jeffry A. Simpson. "The Dual Impact of Early and Concurrent Life Stress on Adults’ Diurnal Cortisol Patterns: A Prospective Study." Psychological Science 30, no. 5 (March 8, 2019): 739–47. http://dx.doi.org/10.1177/0956797619833664.
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Major life stress often produces a flat diurnal cortisol slope, an indicator of potential long-term health problems. Exposure to stress early in childhood or the accumulation of stress across the life span may be responsible for this pattern. However, the relative impact of life stress at different life stages on diurnal cortisol is unknown. Using a longitudinal sample of adults followed from birth, we examined three models of the effect of stress exposure on diurnal cortisol: the cumulative model, the biological-embedding model, and the sensitization model. As its name implies, the cumulative model focuses on cumulative life stress. In contrast, the biological-embedding model implicates early childhood stress, and the sensitization model posits that current life stress interacts with early life stress to produce flat diurnal cortisol slopes. Our analyses are consistent with the sensitization model, as they indicate that the combination of high stress exposure early in life and high current stress predict flat diurnal cortisol slopes. These novel findings advance understanding of diurnal cortisol patterns and point to avenues for intervention.
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Li, Ximing, Jiaojiao Zhang, and Jihong Ouyang. "Dirichlet Multinomial Mixture with Variational Manifold Regularization: Topic Modeling over Short Texts." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7884–91. http://dx.doi.org/10.1609/aaai.v33i01.33017884.
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Conventional topic models suffer from a severe sparsity problem when facing extremely short texts such as social media posts. The family of Dirichlet multinomial mixture (DMM) can handle the sparsity problem, however, they are still very sensitive to ordinary and noisy words, resulting in inaccurate topic representations at the document level. In this paper, we alleviate this problem by preserving local neighborhood structure of short texts, enabling to spread topical signals among neighboring documents, so as to correct the inaccurate topic representations. This is achieved by using variational manifold regularization, constraining the close short texts should have similar variational topic representations. Upon this idea, we propose a novel Laplacian DMM (LapDMM) topic model. During the document graph construction, we further use the word mover’s distance with word embeddings to measure document similarities at the semantic level. To evaluate LapDMM, we compare it against the state-of-theart short text topic models on several traditional tasks. Experimental results demonstrate that our LapDMM achieves very significant performance gains over baseline models, e.g., achieving even about 0.2 higher scores on clustering and classification tasks in many cases.
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Nazarenko, D. S., I. V. Afanasieva, and N. V. Golian. "NEURAL NETWORK APPROACH FOR EMOTIONAL RECOGNITION IN TEXT." Bionics of Intelligence 1, no. 92 (June 2, 2019): 9–13. http://dx.doi.org/10.30837/bi.2019.1(92).02.
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The article is devoted to one of the most popular trends in the field of IT today – natural language processing, in particular, the extraction of emotions from the text using the neural network approach. The main task was to solve the problem of the high costs of time and human resources for companies to receive feedback from users and process emotional reactions of the second one. That to decide the task it was necessary to make modelling and learn neural network using own architecture based on the backpropagation algorithm that to recognize the emotional component in the text.The emotional component of reviews was used as a metric for evaluating user reactions. It was decided to work with five types of emotions that will help to provide better results. The neural network architecture consists of interconnected layers: embedding, bidirectional LSTM, pooling, dropout layers and two dense layers. For the neural network learning was selected an open dataset consisted of 47,288-tagged posts from Twitter. As a result, the F-measure on the test dataset was 0.62 and which is a worthy indicator in comparison with large business solutions.
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Arachie, Chidubem, Manas Gaur, Sam Anzaroot, William Groves, Ke Zhang, and Alejandro Jaimes. "Unsupervised Detection of Sub-Events in Large Scale Disasters." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 354–61. http://dx.doi.org/10.1609/aaai.v34i01.5370.
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Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people “on the ground” post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. Emergency responders are largely interested in finding out what events are taking place so they can properly plan and deploy resources. In this paper we address the problem of automatically identifying important sub-events (within a large-scale emergency “event”, such as a hurricane). In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. We first extract noun-verb pairs and phrases from raw tweets as sub-event candidates. Then, we learn a semantic embedding of extracted noun-verb pairs and phrases, and rank them against a crisis-specific ontology. We filter out noisy and irrelevant information then cluster the noun-verb pairs and phrases so that the top-ranked ones describe the most important sub-events. Through quantitative experiments on two large crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we demonstrate the effectiveness of our approach over the state-of-the-art. Our qualitative evaluation shows better performance compared to our baseline.
Dissertations / Theses on the topic "Post's embedding problem"
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Chambart, Pierre. "On Post's embedding problem and the complexity of lossy channels." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2011. http://tel.archives-ouvertes.fr/tel-00777541.
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Lossy channel systems were originally introduced to model communication protocols. It gave birth to a complexity class wich remained scarcely undersood for a long time. In this thesis we study some of the most important gaps. In particular, we bring matching upper and lower bounds for the time complexity. Then we describe a new proof tool : the Post Embedding Problem (PEP) which is a simple problem, closely related to the Post Correspondence Problem, and complete for this complexity class. Finally, we study PEP, its variants and the languages of solutions of PEP on which we provide complexity results and proof tools like pumping lemmas.
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Chambart, Pierre. "On Post’s embedding problem and the complexity of lossy channels." Thesis, Cachan, Ecole normale supérieure, 2011. http://www.theses.fr/2011DENS0036/document.
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Les systèmes à canaux non fiables ont été introduits à l'origine comme un modèle de communication. Ils ont donné naissance à une classe de complexité restée mal comprise pendant longtemps. Dans cette thèse, nous étudions et comblons certaines des plus importantes lacunes dans la connaissance de cette classe. Nous fournissons entre autres des bornes inférieure et supérieure qui se rejoignent pour la complexité en temps. Puis nous proposons un nouvel outil de preuve : le Problème de Sous Mot de Post (PEP). C'est un problème simple, inspiré du Problème de Correspondance de Post, et complet pour cette classe de complexité. Nous étudions ensuite PEP et ses variantes, ainsi que les langages de solutions de PEP sur lesquels nous avons fourni des résultats de complexité et des outils de preuve tels que des lemmes de pompageLossy channel systems were originally introduced to model communication protocols. It gave birth to a complexity class wich remained scarcely undersood for a long time. In this thesis we study some of the most important gaps. In particular, we bring matching upper and lower bounds for the time complexity. Then we describe a new proof tool : the Post Embedding Problem (PEP) which is a simple problem, closely related to the Post Correspondence Problem, and complete for this complexity class. Finally, we study PEP, its variants and the languages of solutions of PEP on which we provide complexity results and proof tools like pumping lemmas
Book chapters on the topic "Post's embedding problem"
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Koltai, Júlia, Zoltán Kmetty, and Károly Bozsonyi. "From Durkheim to Machine Learning: Finding the Relevant Sociological Content in Depression and Suicide-Related Social Media Discourses." In Pathways Between Social Science and Computational Social Science, 237–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54936-7_11.
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AbstractThe phenomenon of suicide has been a focal point since Durkheim among social scientists. Internet and social media sites provide new ways for people to express their positive feelings, but they are also platforms to express suicide ideation or depressed thoughts. Most of these posts are not about real suicide, and some of them are a cry for help. Nevertheless, suicide- and depression-related content varies among platforms, and it is not evident how a researcher can find these materials in mass data of social media. Our paper uses the corpus of more than four million Instagram posts, related to mental health problems. After defining the initial corpus, we present two different strategies to find the relevant sociological content in the noisy environment of social media. The first approach starts with a topic modeling (Latent Dirichlet Allocation), the output of which serves as the basis of a supervised classification method based on advanced machine-learning techniques. The other strategy is built on an artificial neural network-based word embedding language model. Based on our results, the combination of topic modeling and neural network word embedding methods seems to be a promising way to find the research related content in a large digital corpus.Our research can provide added value in the detection of possible self-harm events. With the utilization of complex techniques (such as topic modeling and word embedding methods), it is possible to identify the most problematic posts and most vulnerable users.
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Yinka-Banjo, Chika, Gafar Lekan Raji, and Ifeanyi Precious Ohalete. "Auto-Detection of Human Factor Contents on Social Media Posts Using Word2vec and Long Short-Term Memory (LSTM)." In Handbook of Research on the Role of Human Factors in IT Project Management, 1–13. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1279-1.ch001.
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The threat posed by cyberbullying to the mental health in our society cannot be overemphasized. Victims of this menace are reported to have suffered poor academic performance, depression, and suicidal thoughts. There is need to find an efficient and effective solution to this problem within the academic environment. In this research, one of the popular deep learning models—long short-term memory (LSTM)—known for its optimized performance in training sequential data was combined with Word2Vec embedding technique to create a model trained for classifying the content of social media post as containing cyberbullying content or otherwise. The result was observed to have shown improvements in its performance with respect to accuracy in the classification task with over 80% of the test dataset correctly classified as against the existing model with about 74.9% accuracy.
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Groeneboer, Chris, and Monika Whitney. "An Overview of Knowledge Translation." In Machine Learning, 77–86. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch107.
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Knowledge translation (KT) was traditionally framed as a problem of moving research results into policy and practice. The impetus for the flow of knowledge originated with researchers constructing new knowledge and seeing its utility, or with policymakers and administrators seeing problems in practice and looking to researchers for solutions. In the 1970s, a shift in focus away from knowledge use was exemplified by Caplan’s (1979) two-communities theory, which posits that researchers and policymakers comprise two different communities with two different languages (Jacobson, Butterill, & Goering, 2003). A shift back to knowledge use with a new focus on user-centered design is evident in more recent KT models that provide frameworks for researcher and user interaction in order to build better understanding between diverse groups. The flow of knowledge from its construction in one context to its use in another context has been variously termed knowledge translation, knowledge exchange, knowledge transfer, research transfer, technology transfer, knowledge transformation, knowledge dissemination, knowledge mobilization, knowledge utilization, and research utilization. The terms are often used synonymously, but a specific term is sometimes used because it highlights a particular component of the knowledge flow process. For example, knowledge exchange implies a sharing of information between partners of equal value and focuses on the movement of knowledge between them, whereas research utilization implies the transformation of research results into usable knowledge and focuses on embedding the usable knowledge in practice. Information technologies have the potential to support knowledge translation in powerful ways. Key processes in the translation of knowledge include: (1) knowledge creation, management, and dissemination; (2) recognition of links between existing knowledge and its potential application to problems or practice; (3) translation into usable knowledge in practice; and (4) change in practice. Information technologies are a natural solution for these knowledge translation processes. For example, group and social software such as blogs and wikis support collaborative construction and sharing of knowledge; knowledge management systems support capture, storage, accessibility, and maintenance of constructed knowledge; and most Internet-based technologies support dissemination of information. Well-designed virtual communities provide online environments for the kinds of human interaction that enable collaborative exploration of ideas, that foster recognition of potential links between existing knowledge and its application to solve problems or change practice, and that inspire people to transform their practice. Data mining and artificial intelligence techniques can be used to enhance identification of potential links between knowledge in one context and problems in another context.
Conference papers on the topic "Post's embedding problem"
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Huang, Haoran, Qi Zhang, and Xuanjing Huang. "Mention Recommendation for Twitter with End-to-end Memory Network." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/260.
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In this study, we investigated the problem of recommending usernames when people attempt to use the ``@'' sign to mention other people in twitter-like social media. With the extremely rapid development of social networking services, this problem has received considerable attention in recent years. Previous methods have studied the problem from different aspects. Because most of Twitter-like microblogging services limit the length of posts, statistical learning methods may be affected by the problems of word sparseness and synonyms. Although recent progress in neural word embedding methods have advanced the state-of-the-art in many natural language processing tasks, the benefits of word embedding have not been taken into consideration for this problem. In this work, we proposed a novel end-to-end memory network architecture to perform this task. We incorporated the interests of users with external memory. A hierarchical attention mechanism was also applied to better consider the interests of users. The experimental results on a dataset we collected from Twitter demonstrated that the proposed method could outperform state-of-the-art approaches.
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Zhao, Zhou, Hanqing Lu, Deng Cai, Xiaofei He, and Yueting Zhuang. "Microblog Sentiment Classification via Recurrent Random Walk Network Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/494.
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Microblog Sentiment Classification (MSC) is a challenging task in microblog mining, arising in many applications such as stock price prediction and crisis management. Currently, most of the existing approaches learn the user sentiment model from their posted tweets in microblogs, which suffer from the insufficiency of discriminative tweet representation. In this paper, we consider the problem of microblog sentiment classification from the viewpoint of heterogeneous MSC network embedding. We propose a novel recurrent random walk network learning framework for the problem by exploiting both users’ posted tweets and their social relations in microblogs. We then introduce the deep recurrent neural networks with random-walk layer for heterogeneous MSC network embedding, which can be trained end-to-end from the scratch. Weemploytheback-propagationmethodfortraining the proposed recurrent random walk network model. The extensive experiments on the large-scale public datasets from Twitter show that our method achieves better performance than other state-of-the-art solutions to the problem.
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Giabelli, Anna, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, and Andrea Seveso. "Skills2Graph: Processing million Job Ads to face the Job Skill Mismatch Problem." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/708.
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In this paper, we present Skills2Graph, a tool that, starting from a set of users’ professional skills, identifies the most suitable jobs as they emerge from a large corpus of 2.5M+ Online Job Vacancies (OJVs) posted in three different countries (the United Kingdom, France, and Germany). To this aim, we rely both on co-occurrence statistics - computing a count-based measure of skill-relevance named Revealed Comparative Advantage (rca) - and distributional semantics - generating several embeddings on the OJVs corpus and performing an intrinsic evaluation of their quality. Results, evaluated through a user study of 10 labor market experts, show a high P@3 for the recommendations provided by Skills2Graph, and a high nDCG (0.985 and 0.984 in a [0,1] range), that indicates a strong correlation between the experts’ scores and the rankings generated by Skills2Graph.
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Smalc, Martin, Prathib Skandakumaran, and Julian Norley. "Thermal Performance of Natural Graphite Heat Spreaders With Embedded Thermal Vias." In ASME 2007 InterPACK Conference collocated with the ASME/JSME 2007 Thermal Engineering Heat Transfer Summer Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/ipack2007-33215.
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Natural graphite heat spreaders are in use in electronic cooling applications where heat flux density is low. Natural graphite is an anisotropic material, with a high thermal conductivity in the plane of the spreader combined with a much lower thermal conductivity through its thickness. This low through-thickness thermal conductivity poses a problem when attempting to cool heat sources with relatively high heat flux densities. This problem can be overcome by embedding a thermal via in the graphite material. This via is made from an isotropic material with a thermal conductivity significantly higher than the through-thickness graphite conductivity. This paper examines the thermal performance of a natural graphite heat spreader with an embedded thermal via. The work is primarily experimental although numerical models were used to guide the experiments. The thermal performance of these spreaders is compared to that of spreaders made from conventional isotropic materials. The effect of accelerated aging tests on the performance of these graphite spreaders is reviewed. Finally, two applications are examined; first cooling an ASIC module and second, cooling an FB-DIMM memory card.
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Fujita, Kikuo. "Embedding Real Engineering Decisions and Sometimes-Consequent Errors in a Small Design Project Toward Reflective Learning." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dtm-34003.
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This paper reports an intention and experiences in introducing a project-based design class. While project-based learning is a widely recognized framework for design education, its methodology is still under discussion. Department of Mechanical Engineering at Osaka University starts a new design class for junior students under the demands arisen for Japanese universities and by focusing a latest meaning of knowledge process. The class is characterized by the possibility of errors consequent on the students’ decisions in system-level design. It aims to make the students learn some design paradigms through reflection of events in live situation. For this direction, the students solve a peculiarly simplified but contextually rich design problem by executing all phases of the entire design process, produce hardware, use them, and do post-analysis in two months. The project’s mission and associated schedule are arranged for students to implicitly confront tradeoffs across engineering disciplines and the entire engineering process. After the class it is observed that the students began to learn the naiveness of individual knowledge in system-level design, how it occurs, how it can be avoided, and so forth from their own experiences. The paper is concluded with discussion on the prospects in this type of project-based design education.