A Survey of Languages for Specifying Dynamics: A Knowledge Engineering Perspective

A number of formal specification languages for knowledge-based systems has been developed. Characteristics for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects. They have to provide the means to specify a complex and large amount of knowledge and they have to provide the means to specify the dynamic reasoning behavior of a knowledge-based system. We focus on the second aspect. For this purpose, we survey existing approaches for specifying dynamic behavior in related areas of research. In fact, we have taken approaches for the specification of information systems (Language for Conceptual Modeling and TROLL), approaches for the specification of database updates and logic programming (Transaction Logic and Dynamic Database Logic) and the generic specification framework of abstract state machine

Normal mere exposure effect with impaired recognition in Alzheimer’s disease.

We investigated the mere exposure effect and the explicit memory in Alzheimer’s disease (AD) patients and elderly control subjects, using unfamiliar faces. During the exposure phase, the subjects estimated the age of briefly flashed faces. The mere exposure effect was examined by presenting pairs of faces (old and new) and asking participants to select the face they liked. The participants were then presented with a forced-choice explicit recognition task. Controls subjects exhibited above-chance preference and recognition scores for old faces. The AD patients also showed the mere exposure effect but no explicit recognition. These results suggest that the processes involved in the mere exposure effect are preserved in AD patients despite their impaired explicit recognition. The results are discussed in terms of Seamon et al.’s proposal (1995) that processes involved in the mere exposure effect are equivalent to those subserving perceptual priming. These processes would depend on extrastriate areas which are relatively preserved in AD patients

Evolutionary Channels for the Formation of Double Neutron Stars

We analyze binary population models of double-neutron stars and compare results to the accurately measured orbital periods and eccentricities of the eight known such systems in our Galaxy. In contrast to past similar studies, we especially focus on the dominant evolutionary channels (we identify three); for the first time, we use a detailed understanding of the evolutionary history of three double neutron stars as actual constraints on the population models. We find that the evolutionary constraints derived from the double pulsar are particularly tight, and less than half of the examined models survive the full set of constraints. The top-likelihood surviving models yield constraints on the key binary evolution parameters, but most interestingly reveal (i) the need for electron-capture supernovae from relatively low-mass degenerate, progenitor cores, and (ii) the most likely evolutionary paths for the rest of the known double neutron stars. In particular, we find that J1913+16 likely went through a phase of Case BB mass transfer, and J1906+0746 and J1756-2251 are consistent with having been formed in electron-capture supernovae.Comment: 17 pages, 9 figure

Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping

In this paper, we provide for the first time a systematic comparison of distribution matching (DM) and sphere shaping (SpSh) algorithms for short blocklength probabilistic amplitude shaping. For asymptotically large blocklengths, constant composition distribution matching (CCDM) is known to generate the target capacity-achieving distribution. As the blocklength decreases, however, the resulting rate loss diminishes the efficiency of CCDM. We claim that for such short blocklengths and over the additive white Gaussian channel (AWGN), the objective of shaping should be reformulated as obtaining the most energy-efficient signal space for a given rate (rather than matching distributions). In light of this interpretation, multiset-partition DM (MPDM), enumerative sphere shaping (ESS) and shell mapping (SM), are reviewed as energy-efficient shaping techniques. Numerical results show that MPDM and SpSh have smaller rate losses than CCDM. SpSh--whose sole objective is to maximize the energy efficiency--is shown to have the minimum rate loss amongst all. We provide simulation results of the end-to-end decoding performance showing that up to 1 dB improvement in power efficiency over uniform signaling can be obtained with MPDM and SpSh at blocklengths around 200. Finally, we present a discussion on the complexity of these algorithms from the perspective of latency, storage and computations.Comment: 18 pages, 10 figure

A comparison of population types used for QTL mapping in Arabidopsis thaliana

In Arabidopsis, a variety of mapping populations have been used for the detection of quantitative trait loci (QTLs) responsible for natural variation. In this study, we presentan overview of the advantages and disadvantages of the different types of populations used. To do this, we compare the results of both experimental and natural populations for the commonly analysed trait flowering time. It is expected that genome wide association (GWA) mapping will be an increasingly important tool for QTL mapping because of the high allelic richness and mapping resolution in natural populations. In Arabidopsis, GWA mapping becomes ever more facilitated by the increasing availability of re-sequenced genomes of many accessions. However, specifically designed mapping populations such as recombinant inbred lines and near isogenic lines will remain important. The high QTL detection power of such experimental populations can identify spurious GWA associations, and their unique genomic structure is superior for investigating the role of low-frequency alleles. Future QTL studies will therefore benefit from a combined approach of GWA and classical linkage analysis

Re-Appreciating the Why of Cognition: 35 Years after Marr and Poggio

Marr and Poggio’s levels of description are one of the most well-known theoretical constructs of twentieth century cognitive science. It entails that behavior can and should be considered at three different levels: computation, algorithm, and implementation. In this contribution focus is on the computational level of description, the level that describes the “why” of cognition. I argue that the computational level should be taken as a starting point in devising experiments in cognitive (neuro)science. Instead, the starting point in empirical practice often is a focus on the stimulus or on some capacity of the cognitive system. The “why” of cognition tends to be ignored when designing research, and is not considered in subsequent inference from experimental results. The overall aim of this manuscript is to show how re-appreciation of the computational level of description as a starting point for experiments can lead to more informative experimentation