217 research outputs found
Information recovery from rank-order encoded images
The work described in this paper is inspired by SpikeNET, a system
developed to test the feasibility of using rank-order codes in modelling largescale
networks of asynchronously spiking neurons. The rank-order code theory
proposed by Thorpe concerns the encoding of information by a population of
spiking neurons in the primate visual system. The theory proposes using the order
of firing across a network of asynchronously firing spiking neurons as a neural
code for information transmission. In this paper we aim to measure the perceptual
similarity between the image input to a model retina, based on that originally
designed and developed by VanRullen and Thorpe, and an image reconstructed
from the rank-order encoding of the input image. We use an objective metric
originally proposed by Petrovic to estimate perceptual edge preservation in image
fusion which, after minor modifcations, is very much suited to our purpose. The
results show that typically 75% of the edge information of the input stimulus is
retained in the reconstructed image, and we show how the available information
increases with successive spikes in the rank-order code
Case Adaptation with Qualitative Algebras
This paper proposes an approach for the adaptation of spatial or temporal
cases in a case-based reasoning system. Qualitative algebras are used as
spatial and temporal knowledge representation languages. The intuition behind
this adaptation approach is to apply a substitution and then repair potential
inconsistencies, thanks to belief revision on qualitative algebras. A temporal
example from the cooking domain is given. (The paper on which this extended
abstract is based was the recipient of the best paper award of the 2012
International Conference on Case-Based Reasoning.
The use of a quantitative structure-activity relationship (QSAR) model to predict GABA-A receptor binding of newly emerging benzodiazepines
The illicit market for new psychoactive substances is forever expanding. Benzodiazepines and their derivatives are one of a number of groups of these substances and thus far their number has grown year upon year. For both forensic and clinical purposes it is important to be able to rapidly understand these emerging substances. However as a consequence of the illicit nature of these compounds, there is a deficiency in the pharmacological data available for these ānewā benzodiazepines. In order to further understand the pharmacology of ānewā benzodiazepines we utilised a quantitative structure-activity relationship (QSAR) approach. A set of 69 benzodiazepine-based compounds was analysed to develop a QSAR training set with respect to published binding values to GABAA receptors. The QSAR model returned an R2 value of 0.90. The most influential factors were found to be the positioning of two H-bond acceptors, two aromatic rings and a hydrophobic group. A test set of nine random compounds was then selected for internal validation to determine the predictive ability of the model and gave an R2 value of 0.86 when comparing the binding values with their experimental data. The QSAR model was then used to predict the binding for 22 benzodiazepines that are classed as new psychoactive substances. This model will allow rapid prediction of the binding activity of emerging benzodiazepines in a rapid and economic way, compared with lengthy and expensive in vitro/in vivo analysis. This will enable forensic chemists and toxicologists to better understand both recently developed compounds and prediction of substances likely to emerge in the future
Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems
The modelling, analysis, and visualisation of dynamic geospatial phenomena
has been identified as a key developmental challenge for next-generation
Geographic Information Systems (GIS). In this context, the envisaged
paradigmatic extensions to contemporary foundational GIS technology raises
fundamental questions concerning the ontological, formal representational, and
(analytical) computational methods that would underlie their spatial
information theoretic underpinnings.
We present the conceptual overview and architecture for the development of
high-level semantic and qualitative analytical capabilities for dynamic
geospatial domains. Building on formal methods in the areas of commonsense
reasoning, qualitative reasoning, spatial and temporal representation and
reasoning, reasoning about actions and change, and computational models of
narrative, we identify concrete theoretical and practical challenges that
accrue in the context of formal reasoning about `space, events, actions, and
change'. With this as a basis, and within the backdrop of an illustrated
scenario involving the spatio-temporal dynamics of urban narratives, we address
specific problems and solutions techniques chiefly involving `qualitative
abstraction', `data integration and spatial consistency', and `practical
geospatial abduction'. From a broad topical viewpoint, we propose that
next-generation dynamic GIS technology demands a transdisciplinary scientific
perspective that brings together Geography, Artificial Intelligence, and
Cognitive Science.
Keywords: artificial intelligence; cognitive systems; human-computer
interaction; geographic information systems; spatio-temporal dynamics;
computational models of narrative; geospatial analysis; geospatial modelling;
ontology; qualitative spatial modelling and reasoning; spatial assistance
systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964);
Special Issue on: Geospatial Monitoring and Modelling of Environmental
Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press
Inconsistency-tolerant Query Answering in Ontology-based Data Access
Ontology-based data access (OBDA) is receiving great attention as a new paradigm for managing information systems through semantic technologies. According to this paradigm, a Description Logic ontology provides an abstract and formal representation of the domain of interest to the information system, and is used as a sophisticated schema for accessing the data and formulating queries over them. In this paper, we address the problem of dealing with inconsistencies in OBDA. Our general goal is both to study DL semantical frameworks that are inconsistency-tolerant, and to devise techniques for answering unions of conjunctive queries under such inconsistency-tolerant semantics. Our work is inspired by the approaches to consistent query answering in databases, which are based on the idea of living with inconsistencies in the database, but trying to obtain only consistent information during query answering, by relying on the notion of database repair. We first adapt the notion of database repair to our context, and show that, according to such a notion, inconsistency-tolerant query answering is intractable, even for very simple DLs. Therefore, we propose a different repair-based semantics, with the goal of reaching a good compromise between the expressive power of the semantics and the computational complexity of inconsistency-tolerant query answering. Indeed, we show that query answering under the new semantics is first-order rewritable in OBDA, even if the ontology is expressed in one of the most expressive members of the DL-Lite family
Unify Markov model for Rational Design and Synthesis of More Safe Drugs. Predicting Multiple Drugs Side Effects
The 9th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryMost of present mathematical models for rational design and synthesis of new drugs consider just the molecular structure. In the present article we pretend extending the use of Markov Chain models to define novel molecular descriptors, which consider in addition other parameters like target site or biological effect. Specifically, this model takes into consideration not only the molecular structure but the specific biological system the drug affects too. Herein, it is developed a general Markov model that describes 19 different drugs side effects grouped in 8 affected biological systems for 178 drugs, being 270 cases finally. The data was processed by Linear Discriminant Analysis (LDA) classifying drugs according to their specific side effects, forward stepwise was fixed as strategy for variables selection. The average percentage of good classification and number of compounds used in the training/predicting sets were 100/95.8% for endocrine manifestations(18 out of 18)/(13 out of 14); 90.5/92.3% for gastrointestinal manifestations (38 out of 42)/(30 out of 32); 88.5/86.5% for systemic phenomena (23 out of 26)/(17 out of 20); 81.8/77.3% for neurological manifestations (27 out of 33)/(19 out of 25); 81.6/86.2% for dermal manifestations (31 out of 38)/(25 out of 29); 78.4/85.1% for cardiovascular manifestation (29 out of 37)/(24 out of 28); 77.1/75.7% for breathing manifestations (27 out of 35)/(20 out of 26) and 75.6/75% for psychiatric manifestations (31 out of 41)/(23 out of 31). Additionally a Back-Projection Analysis (BPA) was carried out for two ulcerogenic drugs to prove in structural terms the physic interpretation of the models obtained. This article develops a model that encompasses a large number of drugs side effects grouped in specifics biological systems using stochastic absolute probabilities of interaction (Apk (j)) by the first time
Structural feature based computational approach of toxicity prediction of ionic liquids: Cationic and anionic effects on ionic liquids toxicity
yesThe density functional theory (DFT) based a unique model has been developed to predict the toxicity of ionic liquids using structural-feature based quantum chemical reactivity descriptors. Electrophilic indices (Ļ), the energy of highest occupied (EHOMO) and lowest unoccupied molecular orbital, (ELUMO) and energy gap (ā E) were selected as the best toxicity descriptors of ILs via Pearson correlation and multiple linear regression analyses. The principle components analysis (PCA) demonstrated the distribution and inter-relation of descriptors of the model. A multiple linear regression (MLR) analysis on selected descriptors derived the model equation for toxicity prediction of ionic liquids. The model predicted toxicity values and mechanism are very consistent with observed toxicity. Cationic and side chains length effect are pronounced to the toxicity of ILs. The model will provide an economic screening method to predict the toxicity of a wide range of ionic liquids and their toxicity mechanism
Information recovery from rank-order encoded images
The time to detection of a visual stimulus by the primate eye is recorded at
100 ā 150ms. This near instantaneous recognition is in spite of the considerable
processing required by the several stages of the visual pathway to recognise and
react to a visual scene. How this is achieved is still a matter of speculation.
Rank-order codes have been proposed as a means of encoding by the primate
eye in the rapid transmission of the initial burst of information from the sensory
neurons to the brain. We study the efficiency of rank-order codes in encoding
perceptually-important information in an image. VanRullen and Thorpe built a
model of the ganglion cell layers of the retina to simulate and study the viability
of rank-order as a means of encoding by retinal neurons. We validate their model
and quantify the information retrieved from rank-order encoded images in terms
of the visually-important information recovered. Towards this goal, we apply
the āperceptual information preservation algorithmā, proposed by Petrovic and
Xydeas after slight modification. We observe a low information recovery due
to losses suffered during the rank-order encoding and decoding processes. We
propose to minimise these losses to recover maximum information in minimum
time from rank-order encoded images. We first maximise information recovery by
using the pseudo-inverse of the filter-bank matrix to minimise losses during rankorder
decoding. We then apply the biological principle of lateral inhibition to
minimise losses during rank-order encoding. In doing so, we propose the Filteroverlap
Correction algorithm. To test the perfomance of rank-order codes in
a biologically realistic model, we design and simulate a model of the foveal-pit
ganglion cells of the retina keeping close to biological parameters. We use this
as a rank-order encoder and analyse its performance relative to VanRullen and
Thorpeās retinal model
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