This week a plug for my new book that just came out: Musical Cognition: A Science of Listening (Read fragments of it online at Google Books; currently available with more than 30% discount on the hardcover at Amazon and Barnes & Noble).From the cover:"Musical Cognition suggests that music is a game (or, in other words, 'benificial play'). In music, our cognitive functions such as perception, memory, attention, and expectation are challenged; yet as listeners we often do not realize that the listener plays an active role in reaching the awareness that makes music so exhilarating, soothing, and inspiring. In reality, the author contends, listening does not happen in the outer world of audible sound but in the inner world of our minds and brains.Recent research in the areas of psychology and neuro-cognition allows Honing to be explicit in a way that many of his predecessors could not. His lucid, evocative writing style guides the reader through what is known about listening to music while avoiding jargon and technical diagrams. With clear examples, the book concentrates on underappreciated musical skills — “sense of rhythm” and “relative pitch” — skills that make us musical creatures. Research on how living creatures respond to music supports the conviction that all humans have a unique, instinctive attraction to music.Musical Cognition includes a selection of intriguing examples from recent literature exploring the role that an implicit or explicit knowledge of music plays when one listens to it. The scope of the topics discussed ranges from the ability of newborns to perceive the beat, to the unexpected musical expertise of ordinary listeners. The evidence shows that music is second nature to most human beings — biologically and socially." Honing, H. (2011) Musical Cognition. A Science of Listening. New Brunswick, N.J.: Transaction Publishers. ISBN 978-1-4128-4228-0.Winkler, I., Haden, G., Ladinig, O., Sziller, I., & Honing, H. (2009). Newborn infants detect the beat in music Proceedings of the National Academy of Sciences, 106 (7), 2468-2471 DOI: 10.1073/pnas.0809035106... Read more »
In a previous post, I considered a proof of the Church-Turing Thesis that Dershowitz and Gurevich published in the Bulletin of Symbolic Logic in 2008. It is safe to say that the proof is controversial — not because it is … Continue reading →... Read more »
Nachum Dershowitz, & Evgenia Falkovich. (2011) A Formalization and Proof of the Extended Church-Turing Thesis. International Workshop on the Development of Computational Models. info:/
The music industry is in flux, has been for at least a decade since the heady days of Napster and Kazaa. A lot of things have changed, the old model of consumers draining their bank accounts to simply consume plastic disks is essentially defunct. File sharing really has put paid to that, but perhaps not [...]Post from: David Bradley's Sciencetext Tech TalkThe music industry is fluxed
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Vishal Midha, Punit Ahluwalia, & and Jerald Hughes. (2011) A new revenue model: a different approach to reduce music piracy. Int. J. Electronic Finance, 5(3), 249-260. info:/
Google has decided to wind down its “labs”, the section of its operations from whence the experimental, developmental applications, such as GMail, Buzz, Wave and, of course, Google+ emerged. They are planning to do it gently, and some labs such as those for GMail and Calendar will persist, but this could be the end of [...]Post from: David Bradley's Sciencetext Tech TalkThe myth of the recreational workplace
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Torkild Thanem, Sara Värlander, & Stephen Cummings. (2011) Open space . Int. J. Work Organisation and Emotion, 4(1), 78-98. info:/
Description of a superluminal communication device... Read more »
Ghirardi, G., Rimini, A., & Weber, T. (1980) A general argument against superluminal transmission through the quantum mechanical measurement process. Lettere al Nuovo Cimento, 27(10), 293-298. DOI: 10.1007/BF02817189
For some time now, the idea of building light-based devices to supplement semiconductor-based computing has attracted the interest of researchers and computer engineers alike. Why? Because, as eloquently put in a 2007 issue of Scientific American, "Light is a wonderful medium for carrying information."... Read more »
Wei H, Wang Z, Tian X, Käll M, & Xu H. (2011) Cascaded logic gates in nanophotonic plasmon networks. Nature communications, 387. PMID: 21750541
Wei H, Li Z, Tian X, Wang Z, Cong F, Liu N, Zhang S, Nordlander P, Halas NJ, & Xu H. (2011) Quantum dot-based local field imaging reveals plasmon-based interferometric logic in silver nanowire networks. Nano letters, 11(2), 471-5. PMID: 21182282
Buffer Personal usage of of the web during work (PWU) is still a matter of debate. Should it be allowed or is it another form of procrastination, disadvantageous to the employers. Should employers limit PWU through monitoring and Internet usage policies? PWU can vary between reading news to watching pornography, is this all bad? Employees [...]
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Anandarajan, M., Simmers, C., & D'Ovidio, R. (2011) Exploring the Underlying Structure of Personal Web Usage in the Workplace. Cyberpsychology, Behavior, and Social Networking, 2147483647. DOI: 10.1089/cyber.2010.0136
You’ve watched all the movies. You’ve read all the books. You’ve even practiced tactial skirmishes with lifesize zombie targets. But now, all of a sudden, you are thinking, “I didn’t know there would be math!” Actually, if you’re a regular … Continue reading →... Read more »
Philip Munz, Ioan Hudea, Joe Imad, & Robert J. Smith?. (2009) When Zombies Attack!: Mathematical Modelling of an Outbreak of Zombie Infection. Infectious Disease Modelling Research Progress, 133-150. info:/
Forking is an important part of Open Source development, and forking is good. Of course, forks should interact too, and genes from one fork should merge back into another fork. Forks are probably also a good indication for the success of a project: if a project is forked, it means it is significant. On the other hand, it can also mean that the main project is too hard to work with. Maybe the CDK is that. Indeed, it's easier to not have your code peer-reviewed, and just fork. That is freedom. (There might be other reasons too.)
The CDK is forked. Forked several time, in fact. I have now started a tracker on SourceForge to aggregate information about these forks, and the state with respect to back-integration of code into our fork. I was aware of the AMBIT fork for a long time, as one of the authors (Nina) has contributed. Of the others I only learned via publications (PaDEL, ScaffoldHunter), and in case of Craft, it was a personal ping that made me aware of it. Craft is all the more exciting because the distributor, Molecular Networks, is primarily know for their proprietary products.
Porting all this code back into the main CDK library is not trivial, and often a lot of work. The current core CDK development team will not be able to do this, and the project relies here on contributions from other to do the integration, and convert code from those forks into proper patches. This is likely interest driven, which is one of the reasons why I started the new tracker. The entries report (briefly) at this moment what interesting functionality is available from those forks, but feel free to add comments with detailed information, such as class names that provide that functionality, so that the CDK community can share the burden of reintegrating this code.
OK, enough for now.
Jeliazkova, N., & Jeliazkov, V. (2011). AMBIT RESTful web services: an implementation of the OpenTox application programming interface Journal of Cheminformatics, 3 (1) DOI: 10.1186/1758-2946-3-18
Wetzel, S., Klein, K., Renner, S., Rauh, D., Oprea, T., Mutzel, P., & Waldmann, H. (2009). Interactive exploration of chemical space with Scaffold Hunter Nature Chemical Biology, 5 (8), 581-583 DOI: 10.1038/nchembio.187
Yap, C. (2011). PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints Journal of Computational Chemistry, 32 (7), 1466-1474 DOI: 10.1002/jcc.21707... Read more »
Jeliazkova, N., & Jeliazkov, V. (2011) AMBIT RESTful web services: an implementation of the OpenTox application programming interface. Journal of Cheminformatics, 3(1), 18. DOI: 10.1186/1758-2946-3-18
Wetzel, S., Klein, K., Renner, S., Rauh, D., Oprea, T., Mutzel, P., & Waldmann, H. (2009) Interactive exploration of chemical space with Scaffold Hunter. Nature Chemical Biology, 5(8), 581-583. DOI: 10.1038/nchembio.187
Yap, C. (2011) PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry, 32(7), 1466-1474. DOI: 10.1002/jcc.21707
Last month, a variety of parenting blogs were in an uproar over the story of a Canadian family that didn’t feel like sharing the sex of newborn Storm with the rest of the world. The media had a field day with the notion of raising a “genderless” child, even after Storm’s mother published an explanation making it clear that their goal was to buffer the child against the relentless gender stereotyping we foist on infants from day one. From garish pink onesies that proclaim “Daddy’s Little Girl” and powder blue “Little Man” t-shirts, to letting our girls’ hair grow out and cutting our boys’ hair short, to offering our girls a doll and our boys a ball, we indicate to our children through subtle and overt actions what their future role might be in society: girl or boy, woman or man.
Within this discussion about de-emphasizing gender norms for the most vulnerable members of our culture—those who are unable to think for themselves—a lot of attention has paid to bucking gendered trends in toys, clothing, and hair style, but only one news piece that I saw brought up the subject of language:
"It is very courageous to challenge [the world] on adjectives that you use on children," [Cheryl] Kilodavis [author of the children’s book My Princess Boy] tells ParentDish. "Instead of saying what a strong boy what a pretty girl, they are saying what a strong or beautiful child."Language is the most important tool that humans ever developed. It allows us to collate and categorize information to make sense of our world, and it allows us to pass on that information to succeeding generations. But language differs around the world – not only in the words used to describe something, but in the number of words used to describe something. That is, the words used by a group of people generally reflect the interests and concerns of those people – so people in cold climates have a larger range of words for cold-weather phenomena than do people living in warm climates, who may have a larger range of words related to their own environment.
This means that language can also differ along gender lines. In a paper that is often assigned in introductory anthropology courses, Daniel Maltz and Ruth Borker discuss the reasons for “male-female miscommunication.” Rather than looking to psychological differences between the sexes to explain differences in communication styles, Maltz and Borker think we should be discussing sociolinguistic subcultures, or the culturally-influenced differences between men’s and women’s approaches to communication. They suggest that women tend to use language to negotiate and express relationships; we tend to use a lot of personal and inclusive pronouns, interject questions and comments in order to show interest; and we are concerned with making segues between topics. On the other hand, jokes and stories are highly valued in men’s speech; loud and aggressive speech is common; and put-downs and insults are normal ways of talking with friends.
What about actual gendered words and phrases? Sure, English, like many languages, has masculine and feminine pronouns, as well as gendered nouns for various relationships and occupations. But we also have more subtly gendered vocabulary, as illustrated in the quote above: we praise our strong boys and our pretty girls. Two researchers at the Center for Mind/Brain Sciences at the University of Trento (Italy) recently decided to empirically test the question of whether there is a gender bias in what women and men talk about. Their goal was not anthropological, but rather computational - to find a way to model “common sense knowledge” as part of the eventual perfection of artificial intelligence (Herdağdelen & Baroni 2011):
Common sense knowledge consists of the simple facts that nearly every person knows but almost never states explicitly because of the very assumption that it is already shared by everyone. Some examples are that mountains are taller than buildings, grocery has a price, or rivers flow downhill. The assumption that common sense knowledge is shared is what allows us to communicate with other people and interact with our surroundings in an efficient and natural way. Therefore, an AI system needs to possess common sense if it aims to interact with people in a natural way.That is, we have all been enculturated into a particular way of life, and we expect people of different ages, occupations, and genders (among other qualities) to interact with us in different ways:
Prejudices and stereotypical knowledge present an intriguing aspect of common sense. As human beings, we rely on (and possibly suffer from) stereotypical expectations. Obviously, we would not want to engineer an AI with its own prejudices and stereotypes, but on the other hand, if an AI system is to relate to humans, it should know about the stereotypical expectations as well—whether it is right or wrong, an AI should know that (we expect that) women like shopping and men like football. Without an explicit knowledge of the stereotypes, such beliefs can be implicit, hidden, and intermixed with other “objective” facts in a knowledge base.The authors, Herdağdelen and Baroni, analyzed a data set consisting of over ten million tweets broadcast from the U.S. in English over Twitter from November 2009 to February 2010. Cross-referencing each Twitter user’s first name with the database of male and female infants’ first names put out by the U.S. Social Security Administration, the authors isolated 5.2 million tweets belonging to men and 5.9 million tweets belonging to women. And they did find gender bias in certain phrases. For example, “[want to] make money” ranked numbers one and three for “masculine” phrases. On the “feminine” side, they found “go [to] bed” and “feel like.” The coolest thing about this research, though, is that the authors set up a nifty online widget – at www.TweetOLife.com – where you can put in any word or phrase you want, to see how it falls along gender lines.
It’s generally assumed that women in American culture distinguish among more color words than men do, possibly as a result of the myriad colors in clothing and makeup. Our parents and our friends likely train us to be aware of these subtleties. Let’s examine this using Tweet-O-Life:
Whereas “red” is basically 50/50, slightly more women than men used the word “maroon” and many more used the word “scarlet.” It’s not a perfect test, of course – those women may be talking about the Scarlet Letter or Scarlet O’Hara. The brilliance of this widget is that you can click over to “detailed query” and find that, while the men are tweeting about “scarlet” with “red,” “knight,” “fever,” and “sin,” the women are tweeting about it with “letter.”
How about language relating to children and childcare? Our “common sense” tells us that women still do the majority of child-rearing.
The term “infant” is the only one that more men say than women, and “toddler” is disproportionately said by women. Interestingly, whereas men used the word “toddler” with words like “autism,” “grandmother,” and “craft,” women used the word with “bed,” “nap,” and “scream.” The diversity of names for children may not be split too heavily along gender lines, but the words used with “toddler” suggest that women may be the primary (naptime?) caregivers.
What if we try something like “computer”? As with “red”, we get basically a 50/50 split between men and women. The really interesting differences come in the detailed query:
Men talk about computers as if they’re actively engaging with them o... Read more »
A. Herdagdelen, & M. Baroni. (2011) Stereotypical gender actions can be extracted from web text. Journal of the American Society for Information Science and Technology. info:/
One of the metaphor recognition papers I read this week had an interesting finding wrt inter-annotator agreement and metaphor: The Automatic Identification of Conceptual Metaphors in Hungarian Texts: A Corpus-based Analysis (Babarczy et a., LREC 2010 Workshop). The purpose of the paper was to run a sort-of bake-off between three methods of creating source/target word lists (to be used by selection preference metaphor recognition system): Three different methods of compiling the word lists were tested: a) word association experiment, b) dictionary of synonyms, and c) reference corpus.Ultimately they found that their corpus based method was most successful as measured by recall/precision, but there was a more striking result rather buried in the paper that I feel deserves more analysis. They created a gold standard by hand-tagging a 30,000 word "baseline" corpus. Here's what they found:At the first attempt, inter-annotator agreement was only 17%. After refining the annotation instructions, we made a second attempt, which resulted in an agreement level of 48%, which is still a strikingly low value. These results indicate that the definition of “metaphoricity” is problematic in itself [emphasis added]. They reported three general sources of inter-annotator DISagreement:Direct vs. Indirect Reference: For example, in the case of the conceptual metaphors ANGER IS HEAT or CONFLICT IS FIRE, the source domain should be an expression referring to a sort of “heated thing”. However, in some cases, one or the other annotator included words indirectly suggesting the presence of heat, such as kiolt ('extinguish'), kihől ( 'get cold') etc.Lexical Ambiguity: For example, the expression eljutottam a mai napig ('I've gotten to this day') may or may not represent a CHANGE IS MOTION metaphor depending on whether the Hungarian verb jut (literally: get somewhere, reach a place by moving the entire body) is taken only to denote physical movement or to be ambiguous.Discrepancies in Classification: ...it is difficult to make an informed decision on whether the following example contains a CHANGE IS MOTION or a PROGRESS IS MOTION FORWARD metaphor, neither of which appear to be an intuitively correct choice: a járvány végigsöpört szülıvárosukon ('the epidemic swept through their hometown').Of the four or five articles I've reviewed on automatic metaphor identification, this is the only one which reported on the results of human-tagging a corpus for metaphor. This strikes me as the sort of thing that should be a first step for anyone seriously interested in this program (certainly anyone interested in the IARPA Metaphor Program).I don't doubt that others have done this, but it seems to be under-reported, suggesting it is not be treated as a core part of the problem.I've complained in my previous posts that there is an overly restricted definition of metaphor underlying contemporary approaches to auto identification, but even within a highly restricted definition like those used by Babarczy et al. and others, there appears to be problems at the heart of identification for humans. So what exactly is being identified?Anna Babarczy, Ildikó Bencze M., István Fekete, & Eszter Simon (2010). The Automatic Identification of Conceptual Metaphors in Hungarian Texts: A Corpus-Based Analysis LREC 2010 Workshop. Proceedings... Read more »
Anna Babarczy, Ildikó Bencze M., István Fekete, & Eszter Simon. (2010) The Automatic Identification of Conceptual Metaphors in Hungarian Texts: A Corpus-Based Analysis. LREC 2010 Workshop. Proceedings. info:/
One of the most common myths about human taste perception is the existence of a “taste map”; stating there are regional differences in sensitivity across the human tongue for sweetness, sourness, bitterness, and saltiness (Fig. 1). Taste maps became popular in the early 20th century; however in the early 21st century, scientists confirmed that all taste qualities are found in all areas of the tongue (e.g. )... Read more »
A quick follow-up to my previous post on automatic metaphor recognition. The paper Automatic Metaphor Recognition Based on Semantic Relation Patterns by Tang et al. challenges the dominant selectional preferences method by substituing their own Semantic Relations Patterns. They point out the problems with Selection Preferences (unfortunately I don't think they solved the problems with their own method, more on that in a bit).Again I'll give the Ling 101, computational linguistics for dummies version (as I understand it ...): Selection Preferences assumes that words frequently co-occur with other words that are literally associated with the same semantic domain. For example,That ship has sailed the mighty ocean.That boat has sailed across lake Erie.That captain has sailed many seas.In these three sentences, the verb sailed occurs with three different subjects (ship, boat, captain) and three different objects (ocean, lake, seas), but all of them evoke the SAILING domain. So a computer could use this info to create a model of the verb sail that would match up the semantics of its expected subjects and objects, then compare them to a new sentence. If the computer encountered the new sentence 4. That student sailed through final exams.It could automatically use the model created from sentences 1-3 above to recognize that the verb sailed occurs with a subject and object not from the SAILING domain, but rather from the STUDENT domain. Then it could use a metaphor mapping component to recognize that HUMANS as MACHINES is an acceptable mapping and thus recognize that #4 might be coherent under a metaphorical interpretation.Tang et al. rightly point out that matching frequency-based selectional preferences is not the same thing as literal meaning. First, they note that some times, a metaphorical pairing is actually MORE FREQUENT than a litertal pairing. They use some Chinese examples, but I think the English translation makes the point. Take the following two uses of close:The plane is close to the tower.Opinion are close.In their corpus, Chinese uses like 'opinions are close' were more frequent, even though this is a non-literal use of close. Frequency would lead the Selectional Preference method to believe that the opinions-type use is literal simply because it is more frequent. This outcome is predicted by Lakoff & Johnson, btw, because one of the core tenants of their seminal work on metaphors was that metaphors are NOT special uses of language, but rather quite common and normal.Tang et al.'s solution is a new method they call Semantic Relation Patterns. Their explanation is brief and highly technical, making it a slog to get through, but it hinges on incorporating an existing semantic relations knowledge base, HowNet, and adding a probabalistic model. Note, I had trouble getting the HowNet website to load, but here is a PDF explanation.How Net is an on-line common-sense knowledge base unveiling inter-conceptual relations and inter-attribute relations of concepts as connoting in Chinese and English bilingual lexicons. In my quick read the two methods differed only minimally in the crucial ways (namely, they are both lexalist and local). Semantic Relation patterns are still based on lexical semantics and still derived entirely locally. I don't see how SRP would handle this metaphor from my earlier post any better than SP:Imagine a situation in a biology class where two students, Alger and Miriam, were originally going to be partners for a lab assignment. Then they got into an argument. A third student, Annette, asks Miriam:Annette: Are you still going to be lab partners with Alger?Miriam: No. That ship has sailed.In this scenario, the sentence "That ship has sailed" is entirely coherent from a selectional preferences perspective (i.e., ships really do sail). Yet it is clearly being used metaphorically (there is literally no ship). Here, the metaphor is only detectable if we link two sentences together via co-reference. The phrase "the ship" does not co-refer to a real ship in the discourse. Rather, it refers to the possible event of be-lab-partners-with-Alger. Unless we can link phrases between sentences and between types (i.e., allowing an NP to co-refer to an event), then we are not going to get a computer to recognize these types of metaphors (which I suspect are quite common).I appreciate Tang et al.'s critique of the SP method and their attempt to get beyond it, but I think their methodology fails to make the critical improvements to automatic metaphor recognition that will be crucial to creating a full scale tool that handles real world metaphor.Xuri Tang, Weiguang Qu, Xiaohe Chen, & Shiwen Yu (2010). Automatic Metaphor Recognition Based on Semantic Relation Patterns International Conference on Asian Language Processing... Read more »
Xuri Tang, Weiguang Qu, Xiaohe Chen, & Shiwen Yu. (2010) Automatic Metaphor Recognition Based on Semantic Relation Patterns. International Conference on Asian Language Processing. info:/
We’ve all had them, emails supposedly from our bank, or an alert from PayPal, perhaps a purported update from Amazon, they sometimes look seriously suspicious, other times you might not be so sure. The grammar and spelling are rarely perfect, they address you as “Dear Subscriber” rather than by name and the worst of all [...]Post from: David Bradley's Sciencetext Tech TalkDon’t get hooked by a phish
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Dharmendra Choukse, Umesh Kumar Singh, & Dimitris Kanellopoulos. (2011) An intelligent anti-phishing solution: password-transaction secure window. Int. J. Internet Technology and Secured Transactions, 3(3), 279-292. info:/
The recently popularized IARPA Metaphor Program piqued my curiosity, so I've been reviewing a variety of articles on contemporary approaches to automatic metaphor identification. I've read three articles so far and one thing is somewhat dissapointing: they all severely restrict the notion of metaphor to mean local metaphors within single sentences.They all pay considerable lip service to Lakoff & Johnson's seminal 1980 work Metaphors We Live By, taking as gospel the notion that metaphor is defined as a mapping from one conceptual domain to another. But their examples are all of a limited type. Here are three representative examples from the papers I've been reading:Achilles was a lion. (Babarczy et al.)The sky is sad. (Tang et al.)I attacked his arguments (Baumer)What struck me is the methods used to identify metaphor are remarkably lexalist. The dominant strategy is Selectional Preferences whereby a list of source and target conceptual domains is created. Then from each, a list of words typically associated with that domain is culled from corpora or intuition or dictionaries. Then, each word is given a set of selectional preferences which constrain what kinds of subjects or predicates it typically occurs with.Here is my Ling 101 version of this methodology: If I understand correctly (and I may not), for Tang et al.'s example "The sky is sad", we would have a concept like THE ENVIRONMENT IS HUMAN. We would have a list of words typically associated with the environment (e.g., "sky") and a list of words typically associated with being human (for example "sad"). A computer could then recognize the following:The subject (the sky) is associated with the environment.The predicate (sad) is associated with humans.This subject (the sky) is not typical for this predicate (sad).This sentence is incoherent on first analysis.The concept THE ENVIRONMENT IS HUMAN links these non-typical phrases coherently.This sentence is only coherent using conceptual mapping, therefore it is probably metaphorical.This is a gross oversimplification, but I think it gets the big picture about right.At first blush, I'm impressed with the simplicity and elegance of this solution. However, it seems to me that much metaphorical language is not local like this (local here = within a single sentence). For example, imagine a situation in a biology class where two students, Alger and Miriam, were originally going to be partners for a lab assignment. Then they got into an argument. A third student, Annette, asks Miriam:Annette: Are you still going to be lab partners with Alger?Miriam: No. That ship has sailed.In this scenario, the sentence "That ship has sailed" is entirely coherent from a selectional preferences perspective (i.e., ships really do sail). Yet it is clearly being used metaphorically (there is literally no ship). Here, the metaphor is only detectable if we link two sentences together via co-reference. The phrase "the ship" does not co-refer to a real ship in the discourse. Rather, it refers to the possible event of be-lab-partners-with-Alger. Unless we can link phrases between sentences and between types (i.e., allowing an NP to co-refer to an event), then we are not going to get a computer to recognize these types of metaphors (which I suspect are quite common).Xuri Tang, Weiguang Qu, Xiaohe Chen, & Shiwen Yu (2010). Automatic Metaphor Recognition Based on Semantic Relation Patterns International Conference on Asian Language ProcessingOther citations:The Automatic Identification of Conceptual Metaphors in Hungarian Texts: ACorpus-Based Analysis. Anna Babarczy, Ildikó Bencze M.1, István Fekete1, Eszter Simon1Computational Metaphor Identification to Foster Critical Thinking and Creativity. ERIC BAUMER (dissertation). 2009.... Read more »
Xuri Tang, Weiguang Qu, Xiaohe Chen, & Shiwen Yu. (2010) Automatic Metaphor Recognition Based on Semantic Relation Patterns. International Conference on Asian Language Processing. info:/
The Church-Turing Thesis lies at the junction between computer science, mathematics, physics and philosophy. The Thesis essentially states that everything computable in the “real world” is exactly what is computable within our accepted mathematical abstractions of computation, such as Turing machines. … Continue reading →... Read more »
Nachum Dershowitz, & Yuri Gurevich. (2008) A Natural Axiomatization of Computability and Proof of Church's Thesis. The Bulletin of Symbolic Logic. DOI: 10.2178/bsl/1231081370
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Wentz CT, Bernstein JG, Monahan P, Guerra A, Rodriguez A, & Boyden ES. (2011) A wirelessly powered and controlled device for optical neural control of freely-behaving animals. Journal of neural engineering, 8(4), 46021. PMID: 21701058
This post is a joint outcome from a couple classes I took in the spring term, on of which was on two-phase fluid flow and the other of which was on scientific communication. The science communication class included a project in which we were to translate a bit of technical literature to a popular science level, and I selected for this purpose the discussion of boiling processes presented in my two-phase flow course. It turns out that boiling processes are a lot more interesting than I'd expected (at least, to me), so on the off chance that anyone else might also find this interesting I've posted my writing project below. Let me know what you think!Read more »... Read more »
Van P. Carey. (2008) Chapter 7: Pool Boiling, Section 1: Regimes of Pool Boiling. Liquid Vapor Phase Change Phenomena: An Introduction to the Thermophysics of Vaporization and Condensation Processes in Heat Transfer Equipment, Second Edition. info:other/978-1591690351
This can only be useful work in the domain of blindness, situation impairment, and accessibility in that it may be possible to convey limited Web page information spatially, dynamically, and with a high degree of comprehension at seven (or nine) times faster because of the ability to comprehend highly parallel speech. Continue reading →... Read more »
Cherry, E. (1953) Some Experiments on the Recognition of Speech, with One and with Two Ears. The Journal of the Acoustical Society of America, 25(5), 975. DOI: 10.1121/1.1907229
Brungart, D., & Simpson, B. (2005) Optimizing the spatial configuration of a seven-talker speech display. ACM Transactions on Applied Perception, 2(4), 430-436. DOI: 10.1145/1101530.1101538
In this post, I will discuss Schaefer’s Theorem for Graphs by Bodirsky and Pinsker, which Michael Pinsker presented at STOC 2011. I love the main proof technique of this paper: start with a finite object, blow it up to an … Continue reading →... Read more »
Manuel Bodirsky, & Michael Pinsker. (2011) Schaefer's Theorem for Graphs. Proceedings of 43rd Annual ACM Symposium on the Theory of Computing. info:/
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