Sentence processing with incremental feedback

Utilizing recurrent network topologies to produce case/role meaning representations for single sentences has become common practice in connectionist natural language processing systems. Typically, these systems train with the complete sentence meaning as the target output for the entire period that the sentence is being processed; i.e., the complete meaning is available starting with the first word of the sentence. Thus, the context feedback provided by these systems is non-incremental in that they use information about the sentence that has not yet been encountered in order to aid in the processing and learning tasks. SAIL1 is a connectionist natural language processing system which builds the sentence meaning representation incrementally, incorporating into the meaning only the information derived from words already processed

Theory formation by abduction : initial results of a case study based on the chemical revolution

Abduction is the process of constructing explanations. This chapter suggests that automated abduction is a key to advancing beyond the "routine theory revision" methods developed in early AI research towards automated reasoning systems capable of "world model revision" — dramatic changes in systems of beliefs such as occur in children's cognitive development and in scientific revolutions. The chapter describes a general approach to automating theory revision based upon computational methods for theory formation by abduction. The approach is based on the idea that, when an anomaly is encountered, the best course is often simply to suppress parts of the original theory thrown into question by the contradiction and to derive an explanation of the anomalous observation based on relatively solid, basic principles. This process of looking for explanations of unexpected new phenomena can lead by abductive inference to new hypotheses that can form crucial parts of a revised theory. As an illustration, the chapter shows how some of Lavoisier's key insights during the Chemical Revolution can be viewed as examples of theory formation by abduction

Theory formation by abduction : a case study based on the chemical revolution

Abduction is the process of constructing explanations. This chapter suggests that automated abduction is a key to advancing beyond the "routine theory revision" methods developed in early AI research towards automated reasoning systems capable of "world model revision" - dramatic changes in systems of beliefs such as occur in children's cognitive development and in scientific revolutions. The chapter describes a general approach to automating theory revision based upon computational methods for theory formation by abduction. The approach is based on the idea that, when an anomaly is encountered, the best course is often simply to suppress parts of the original theory thrown into question by the contradiction and to derive an explanation of the anomalous observation based on relatively solid, basic principles. This process of looking for explanations of unexpected new phenomena can lead by abductive inference to new hypotheses that can form crucial parts of a revised theory. As an illustration, the chapter shows how some of Lavoisier's key insights during the Chemical Revolution can be viewed as examples of theory formation by abduction

aFold – using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data

Data normalization and identification of significant differential expression represent crucial steps in RNA-Seq analysis. Many available tools rely on assumptions that are often not met by real data, including the common assumption of symmetrical distribution of up- and down-regulated genes, the presence of only few differentially expressed genes and/or few outliers. Moreover, the cut-off for selecting significantly differentially expressed genes for further downstream analysis often depend on arbitrary choices

Non-rigid hole band in the extended t-J model

The dispersion of one hole in an extended $t$-$J$ model with additional hopping terms to second and third nearest neighbours and a frustration term in the exchange part has been investigated. Two methods, a Green's function projection technique describing a magnetic polaron of minimal size and the exact diagonalization of a $4*4$ lattice, have been applied, showing reasonable agreement among each other. Using additional hopping integrals which are characteristic for the CuO$_2$ plane in cuprates we find in the nonfrustrated case an isotropic minimum of the dispersion at the point $(\pi/2,\pi/2)$ in $k$-space in good coincidence with recent angle-resolved photoemission results for the insulating compound Sr$_2$CuO$_2$Cl$_2$. Including frustration or finite temperature which shall simulate the effect of doping, the dispersion is drastically changed such that a flat region and an extended saddle point may be observed between $(\pi/2,0)$ and $(\pi,0)$ in agreement with experimental results for the optimally doped cuprates.Comment: 14 pages, LaTeX, 6 figures on request, submitted to Zeitschrift fuer Physi