What are thoughts?

In this dissertation, I investigate a conception of thoughts figuring in ordinary discourse, and argue that this conception is an improvement over a certain standard conception employed in current philosophical and linguistic endeavors. In Chapter 2, I discuss the leading principles of the standard conception, a conception according to which thoughts in general are to be identified with propositions. I also briefly preview some of the main features that distinguish the conception developed in the course of this study from the standard conception. Chapter 4 is the heart of the thesis. I isolate a reading of (forms of) the verb \u27think\u27 that I contend is both familiar from and central to our ordinary discourse about thoughts and thinking--the generic reading , I call it. An investigation of the relation expressed by the verb, \u27think\u27, on this generic reading, and of the correlative conception of thoughts, occupies the remainder of the study. If this ordinary conception of thoughts is to serve the principal functions to which thoughts have standardly been assigned, in philosophical and linguistic endeavors, then there should be a discernible sense in which it is correct to say that thoughts, so conceived, are things that sentences express. In Chapter 5, I discuss the relevant notion of sentential expression. Accounts of this notion have commonly faced a stumbling block in the case of non-declarative sentences. What sort of thoughts, for example, can imperatives or interrogatives be said to express? In Chapter 6, I explain how, on the present conception, there is a clear sense in which sentences of a variety of grammatical moods--imperatives and interrogatives as well as declaratives--may be said, with equal propriety, to express thoughts. In Chapter 7, I discuss a fundamental thesis of the standard conception, and argue, in Chapter 8, that any account of the nature of thoughts accommodating this thesis is incompatible with the conception of thoughts arrived at in Chapter 4. Then, in order to retain this familiar conception and its benefits, a new account of the nature of thoughts must be provided. Such an account is described in Chapter 9

Learning Incoherent Subspaces: Classification via Incoherent Dictionary Learning

In this article we present the supervised iterative projections and rotations (s-ipr) algorithm, a method for learning discriminative incoherent subspaces from data. We derive s-ipr as a supervised extension of our previously proposed iterative projections and rotations (ipr) algorithm for incoherent dictionary learning, and we employ it to learn incoherent sub-spaces that model signals belonging to different classes. We test our method as a feature transform for supervised classification, first by visualising transformed features from a synthetic dataset and from the ‘iris’ dataset, then by using the resulting features in a classification experiment

Development of a cDNA array for chicken gene expression analysis

BACKGROUND: The application of microarray technology to functional genomic analysis in the chicken has been limited by the lack of arrays containing large numbers of genes. RESULTS: We have produced cDNA arrays using chicken EST collections generated by BBSRC, University of Delaware and the Fred Hutchinson Cancer Research Center. From a total of 363,838 chicken ESTs representing 24 different adult or embryonic tissues, a set of 11,447 non-redundant ESTs were selected and added to an existing collection of clones (4,162) from immune tissues and a chicken bursal cell line (DT40). Quality control analysis indicates there are 13,007 useable features on the array, including 160 control spots. The array provides broad coverage of mRNAs expressed in many tissues; in addition, clones with expression unique to various tissues can be detected. CONCLUSIONS: A chicken multi-tissue cDNA microarray with 13,007 features is now available to academic researchers from [email protected]. Sequence information for all features on the array is in GenBank, and clones can be readily obtained. Targeted users include researchers in comparative and developmental biology, immunology, vaccine and agricultural technology. These arrays will be an important resource for the entire research community using the chicken as a model

Low-Rank Regularization for Learning Gene Expression Programs

Learning gene expression programs directly from a set of observations is challenging due to the complexity of gene regulation, high noise of experimental measurements, and insufficient number of experimental measurements. Imposing additional constraints with strong and biologically motivated regularizations is critical in developing reliable and effective algorithms for inferring gene expression programs. Here we propose a new form of regulation that constrains the number of independent connectivity patterns between regulators and targets, motivated by the modular design of gene regulatory programs and the belief that the total number of independent regulatory modules should be small. We formulate a multi-target linear regression framework to incorporate this type of regulation, in which the number of independent connectivity patterns is expressed as the rank of the connectivity matrix between regulators and targets. We then generalize the linear framework to nonlinear cases, and prove that the generalized low-rank regularization model is still convex. Efficient algorithms are derived to solve both the linear and nonlinear low-rank regularized problems. Finally, we test the algorithms on three gene expression datasets, and show that the low-rank regularization improves the accuracy of gene expression prediction in these three datasets