The paper is a discussion of personal risk taking and risk perception in the field of health-related behavior. It reviews work on addictions, which has shown the difficulties of impulse control in the pursuit of long-term commitments. Such long-term commitments are typically in conflict with temporary and strong urges to indulge in seeking pleasure and comfort. People know and do not know, at t...

Although recurrent neural nets have been moderately successful in learning to emulate finite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. DOLCE consists of a standard recurrent neural net train...

Senescence is an integral part of the normal developmental cycle of plants and can be traced in cell, tissue and organ level. This work was focused on the efficiency of post-harvest treatment with the free polyamine spermine (Spm) to extend the vase life of cut rose flowers as an anti-senescence compound. Spm treatment was applied at the rates of 0, 1, 2 and 4 mM. Some morphological, biochemica...

Although recurrent neural nets have been moderately successful in learning to emulate finite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. DOLCE consists of a standard recurrent neural net train...

This paper outlines the reductive modularization and verification of the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE). The ontology makes distinctions between enduring and perduring entities which is reflected in the resulting reductive modules, in contrast to previous work done to generate a consistency proof for DOLCE. We present our approach to verify DOLCE with math...

Although recurrent neural nets have been moderately successful in learning to emulate nite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. dolce consists of a standard recurrent neural net trained...