| ๚ | 2003N88๚iเj16:00`17:00 | |
| ๊ | sๅw๐อค 1K 115บ | |
| uา |
า R็ (Laboratory for Foundations of Computer Science, University of Edinburgh) |
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| ฺ่ | Behavioural Equivalence and Indistinguishability in Higher-Order Typed Languages | |
| Abstract: | ||
| We extend the study of the relationship between behavioural equivalence and indistinguishability relation to the simply typed lambda calculus, where higher-order types are available. The main technical tool of this study is pre-logical relations, which give a precise characterisation of behavioural equivalence. We establish a relationship between behavioural equivalence and indistinguishability in terms of factorisability. We then consider a higher-order logic to reason about models of the simply typed lambda calculus, and relate the resulting standard satisfaction relation to behavioural satisfaction. | ||
| ๚ | QOOQNVPQ๚iเjPUFOO`PVFOO | |
| ๊ | sๅw๐อคQOQบ | |
| uา |
Jean-Philippe Vert isๅwปwคoCICtH}eBNXZ^[j |
|
| ฺ่ | Data mining the proteome in reproducible kernel Hilbert spaces | |
| AuXgNg | ||
|
A major goal of post-genomic era reasearch is to better understand the
functions and interactions of genes discovered during the sequencing
projects recently completed. To this end new technologies such as DNA
chips or Yeast 2-hybrid systems are routinely used to produce large
amounts of data aimed at characterizing each gene.
In order to integrate these various types of information we propose a mathematical framework where a particular type of information is encoded into a kernel function on the set of genes, and where the comparison of two types of information can be performed using linear data mining methods in the corresponding reproducible kernel Hilbert spaces. As an application we show how to extract correlations between microarray data on the one hand, and gene networks on the other hand. |
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ขb๐^โขํนๆF มกBiwณบAtkato@kusm.kyoto-u.ac.jpj ทJ์^li๐อคAhassei@kurims.kyoto-u.ac.jpj |