studies that record neural activations in non-human primates during perceptual decision making tasks have revealed that neural firing rates closely mimic the accumulation of preference theorized by behaviorally-derived diffusion models of decision making. This article bridges the expanse between

that this process can serve as a simple model for organizing behavioral and neural correlates of decision making in more complex contexts, including those that involve economic transactions, social interaction, emotional stimulation, and conscious awareness. Bridging the methodological and theoretical gaps between

of power-sharing, and negotiated decision-making between international, multinational, national, regional, local, and non-governmental, political institutions. There are two common trends in these processes of transformation that, together, signal a new historical landscape. First, none of them could have

models can become large and complex if they include several aspects on a domain and some parts of the data model are volatile. In this paper, we propose a data modeling approach to support ontology users based on ontology building blocks, so-called “Ontology Areas ” (OAs), which allow solving tasks

Abstract: In a recent meeting of IEA's Annex 23, several members presented their conclusions on the modeling of phase change materials behavior in the context of building applications. These conclusions were in agreement with those of a vast review, involving the survey of more than 250

[ A review of past and recent strategies for sub-Nyquist sampling] Signal processing methods have changed substantially over the last several decades. In modern applications, an increasing number of functions is being pushed forward to sophisticated software algorithms, leaving only delicate finely-tuned tasks for the circuit level. Sampling theory, the gate to the digital world, is the key enabling this revolution, encompassing all aspects related to the conversion of continuous-time signals to discrete streams of numbers. The famous Shannon-Nyquist theorem has become a landmark: a mathematical statement which has had one of the most profound impacts on industrial development of digital signal processing (DSP) systems. Over the years, theory and practice in the field of sampling have developed in parallel routes. Contributions by many research groups suggest a multitude of methods, other than uniform sampling, to acquire analog signals [1]–[6]. The math has deepened, leading to abstract signal spaces and innovative sampling techniques. Within generalized sampling theory, bandlimited signals have no special preference, other than historic. At the same time, the market adhered to the Nyquist paradigm; state-of-the-art analog to digital conversion (ADC) devices provide values of their input at equalispaced time points [7], [8]. The footprints of Shannon-Nyquist are evident

the problem of how to build abstractions easily without losing the accuracy of the low-level model. This paper proposes a method of bridging the gap between detailed designs and abstract models,...

Key Words: long term viability; human-landscape interaction; multi-agent based model; decision making. The development of many agricultural regions is uncertain due to current and future changes in economic, social and ecological factors and the complexity of human-landscape interactions

'. The class of material models comprises anything that is a physical entity and that serves as a scientific representation of something else. Among the members of this class we find stock examples like wooden models of bridges, planes, or ships, analogue models like electric circuit models of neural systems

of building an understanding of a complex system by first understanding its parts and then understanding how those parts interact in models of increasing complexity. Learning support for this endeavour was provided by the tools for guiding the development of the sequence of runnable models and for observing