Impact of chronic somatoform and osteoarthritis pain on conscious and preconscious cognitive processing

The study investigates the impact of chronic pain (CP) on conscious and preconscious cognitive processes and on guessing behavior, and examines the mediating effect of a depressive state. Twenty-eight patients with CP due to hip osteoarthritis, 32 patients with a somatoform disorder including pain symptoms, and 31 participants who did not have CP were examined within the framework of a modified Process-Dissociation-Procedure. Neutral, health threatening and general threatening stimuli were presented acoustically in a lexical decision task. Parameters of conscious processing, preconscious processing, and of chance were estimated by a multinomial modelling procedure. CP-patients with osteoarthritis showed the lowest level of conscious processing and the highest level of guessing behavior. Patients with somatoform pain tended to react preconsciously to health threatening stimuli but overall showed a profile similar to that of controls who did not have CP. The impact of the threatening quality of stimuli on different levels of cognitive processing was weak. Depression did not mediate between the experience of pain and estimates of conscious and preconscious processing. Perspective: The impact of CP on preconscious and conscious cognitive processing depends on types and causes of pain. The experience of CP caused by inflammation or physical damage tends to reduce the probability of conscious processing and to provoke memory biases. CP in the context of a somatoform disorder seems to have less impact on cognitive functions

ART-Ada design project, phase 2

Interest in deploying expert systems in Ada has increased. An Ada based expert system tool is described called ART-Ada, which was built to support research into the language and methodological issues of expert systems in Ada. ART-Ada allows applications of an existing expert system tool called ART-IM (Automated Reasoning Tool for Information Management) to be deployed in various Ada environments. ART-IM, a C-based expert system tool, is used to generate Ada source code which is compiled and linked with an Ada based inference engine to produce an Ada executable image. ART-Ada is being used to implement several expert systems for NASA's Space Station Freedom Program and the U.S. Air Force

It goes with the territory: Ownership across spatial boundaries.

Previous studies have shown that people are faster to process objects that they own as compared with objects that other people own. Yet object ownership is embedded within a social environment that has distinct and sometimes competing rules for interaction. Here we ask whether ownership of space can act as a filter through which we process what belongs to us. Can a sense of territory modulate the well-established benefits in information processing that owned objects enjoy? In 4 experiments participants categorized their own or another person’s objects that appeared in territories assigned either to themselves or to another. We consistently found that faster processing of self-owned than other-owned objects only emerged for objects appearing in the self-territory, with no such advantage in other territories. We propose that knowing whom spaces belong to may serve to define the space in which affordances resulting from ownership lead to facilitated processing

EU accession and Poland's external trade policy

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An Architecture for distributed multimedia database systems

In the past few years considerable demand for user oriented multimedia information systems has developed. These systems must provide a rich set of functionality so that new, complex, and interesting applications can be addressed. This places considerable importance on the management of diverse data types including text, images, audio and video. These requirements generate the need for a new generation of distributed heterogeneous multimedia database systems. In this paper we identify a set of functional requirements for a multimedia server considering database management, object synchronization and integration, and multimedia query processing. A generalization of the requirements to a distributed system is presented, and some of our current research and developing activities are discussed

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

The Data Lakehouse: Data Warehousing and More

Relational Database Management Systems designed for Online Analytical Processing (RDBMS-OLAP) have been foundational to democratizing data and enabling analytical use cases such as business intelligence and reporting for many years. However, RDBMS-OLAP systems present some well-known challenges. They are primarily optimized only for relational workloads, lead to proliferation of data copies which can become unmanageable, and since the data is stored in proprietary formats, it can lead to vendor lock-in, restricting access to engines, tools, and capabilities beyond what the vendor offers. As the demand for data-driven decision making surges, the need for a more robust data architecture to address these challenges becomes ever more critical. Cloud data lakes have addressed some of the shortcomings of RDBMS-OLAP systems, but they present their own set of challenges. More recently, organizations have often followed a two-tier architectural approach to take advantage of both these platforms, leveraging both cloud data lakes and RDBMS-OLAP systems. However, this approach brings additional challenges, complexities, and overhead. This paper discusses how a data lakehouse, a new architectural approach, achieves the same benefits of an RDBMS-OLAP and cloud data lake combined, while also providing additional advantages. We take today's data warehousing and break it down into implementation independent components, capabilities, and practices. We then take these aspects and show how a lakehouse architecture satisfies them. Then, we go a step further and discuss what additional capabilities and benefits a lakehouse architecture provides over an RDBMS-OLAP