Dimensions of Neural-symbolic Integration - A Structured Survey

Research on integrated neural-symbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the community to strive for applicable implementations and use cases. Recent work has covered a great variety of logics used in artificial intelligence and provides a multitude of techniques for dealing with them within the context of artificial neural networks. We present a comprehensive survey of the field of neural-symbolic integration, including a new classification of system according to their architectures and abilities.Comment: 28 page

The Integration of Connectionism and First-Order Knowledge Representation and Reasoning as a Challenge for Artificial Intelligence

Intelligent systems based on first-order logic on the one hand, and on artificial neural networks (also called connectionist systems) on the other, differ substantially. It would be very desirable to combine the robust neural networking machinery with symbolic knowledge representation and reasoning paradigms like logic programming in such a way that the strengths of either paradigm will be retained. Current state-of-the-art research, however, fails by far to achieve this ultimate goal. As one of the main obstacles to be overcome we perceive the question how symbolic knowledge can be encoded by means of connectionist systems: Satisfactory answers to this will naturally lead the way to knowledge extraction algorithms and to integrated neural-symbolic systems.Comment: In Proceedings of INFORMATION'2004, Tokyo, Japan, to appear. 12 page

Neural-Symbolic Integration

In this thesis, we discuss different techniques to bridge the gap between two different approaches to artificial intelligence: the symbolic and the connectionist paradigm. Both approaches have quite contrasting advantages and disadvantages. Research in the area of neural-symbolic integration aims at bridging the gap between them. Starting from a human readable logic program, we construct connectionist systems, which behave equivalently. Afterwards, those systems can be trained, and later the refined knowledge be extracted

Hardware-aware block size tailoring on adaptive spacetree grids for shallow water waves.

Spacetrees are a popular formalism to describe dynamically adaptive Cartesian grids. Though they directly yield an adaptive spatial discretisation, i.e. a mesh, it is often more efficient to augment them by regular Cartesian blocks embedded into the spacetree leaves. This facilitates stencil kernels working efficiently on homogeneous data chunks. The choice of a proper block size, however, is delicate. While large block sizes foster simple loop parallelism, vectorisation, and lead to branch-free compute kernels, they bring along disadvantages. Large blocks restrict the granularity of adaptivity and hence increase the memory footprint and lower the numerical-accuracy-per-byte efficiency. Large block sizes also reduce the block-level concurrency that can be used for dynamic load balancing. In the present paper, we therefore propose a spacetree-block coupling that can dynamically tailor the block size to the compute characteristics. For that purpose, we allow different block sizes per spacetree node. Groups of blocks of the same size are identied automatically throughout the simulation iterations, and a predictor function triggers the replacement of these blocks by one huge, regularly rened block. This predictor can pick up hardware characteristics while the dynamic adaptivity of the fine grid mesh is not constrained. We study such characteristics with a state-of-the-art shallow water solver and examine proper block size choices on AMD Bulldozer and Intel Sandy Bridge processors

Россия в условиях современной экономической глобализации

В статье автор пытается определить содержание глобализации как экономической категории. Рассматривает различные подходы к определению глобализации. Выделяет наиболее характерные признаки глобализации, положительные и отрицательные стороны этого явления. На базе анализа глобализации как экономического явления он пытается сделать выводы для российской экономической политики. Акцентирует внимание на необходимости адекватно реагировать на вызовы мировой капиталистической реальности, обращает внимание на то, что абсолютизация рыночных начал дискредитирует идею глобализации. Делает вывод о необходимости усиления внимания к социальным аспектам и противоречиям глобализации с целью минимизации негативных последствий этого явления

Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition

The automatic, sensor-based assessment of challenging behavior of persons with dementia is an important task to support the selection of interventions. However, predicting behaviors like apathy and agitation is challenging due to the large inter- and intra-patient variability. Goal of this paper is to improve the recognition performance by making use of the observation that patients tend to show specific behaviors at certain times of the day or week. We propose to identify such segments of similar behavior via clustering the distributions of annotations of the time segments. All time segments within a cluster then consist of similar behaviors and thus indicate a behavioral predisposition (BPD). We utilize BPDs by training a classifier for each BPD. Empirically, we demonstrate that when the BPD per time segment is known, activity recognition performance can be substantially improved.Comment: Submitted to iWOAR 2022 - 7th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligenc

Free days for future? Longitudinal effects of working time reductions on individual well-being and environmental behaviour

Working time reductions (WTR) are a promising strategy to foster both environmental behaviour and individual well-being. It is unclear, however, whether these possible effects are more likely due to reduced income or to more discretionary time. Moreover, prior studies have only tested the environmental effects of WTR cross-sectionally, and have only tested the well-being effects of WTR including wage compensations. We conducted a longitudinal three-wave study with Swiss employees, including one group who voluntarily reduced their working hours following the first questionnaire. Between-subject analysis suggested that decreased working time is associated with decreased GHG-related behaviours, and increased individual well-being. While the improved GHG-related behaviour is mainly due to reduced income, the well-being effects arise despite lower income. Analyses over time revealed that after reducing their working hours, participants reported increased well-being, more intent-related pro-environmental behaviour, less car commuting, and decreased clothing expenditures. However, no improvement was found regarding other GHG-related behaviours, which are strongly linked to income levels. Thus, reducing standard working time, and simultaneously reducing income, may be a promising strategy. However, voluntarily working a day less per week will probably not reach the full ecological potential of a societal-level WTR