A Quantisation of Cognitive Learning Process by Computer Graphics-Games: Towards More Efficient Learning Models

Research group of Computer Sciences at DMU, Psychology Research Group at University of Birmingham and Reseach Group of Computer Science at University of Northumbria.With the latest developments in computer technologies and artificial intelligence (AI) techniques, more opportunities of cognitive data acquisition and stimulation via game-based systems have become available for computer scientists and psychologists. This may lead to more efficient cognitive learning model developments to be used in different fields of cognitive psychology than in the past. The increasing popularity of computer games among a broad range of age groups leads scientists and experts to seek game domain solutions to cognitive based learning abnormalities, especially for younger age groups and children. One of the major advantages of computer graphics and using game-based techniques over the traditional face-to-face therapies is that individuals, especially children immerse in the game’s virtual environment and consequently feel more open to share their cognitive behavioural characteristics naturally. The aim of this work is to investigate the effects of graphical agents on cognitive behaviours to generate more efficient cognitive models

THE RELATIONSHIP BETWEEN ECONOMIC COMPLEXITY INDEX AND EXPORT: THE CASE OF TURKEY AND CENTRAL ASIAN AND TURKIC REPUBLICS

The paper focuses on the mutual interaction between export from Turkey to Central Asian and Turkic Republics (the CATRs) and exported product range. For measuring the range of exported products, we use economic complexity index (ECI) that refers to the knowledge intensity accumulated in the country's exported products. In addition, ECI provides information regarding the countries' export structures and income levels. We explore how export levels of Turkey and the CATRs, which have common religion and ethnicity, and the countries' ECI scores interact with each other. In this regard, we demonstrate how export affects the countries' ECI for both the CATRs and Turkey. For this purpose, we study the possible relationship between mutual trade volume and the countries' ECI scores by employing Westerlund's cointegration analysis, Pooled Mean Group Estimator (PMGE) model and Dumitrescu-Hurlin's panel causality method. We used the data on the researched countries for the period from 1996 to 2015 collected from official web sites. We have found that export from Turkey to the CATRs and Turkey's ECI scores have a long-term relationship. Additionally, there is a unidirectional causality relationship from Turkey's export to the CATRs to Turkey's ECI score and from the CATRs' ECI scores to the CATRs' export to Turkey. To sum up, our findings support the hypothesis that higher trade volume between Turkey and the CATRs increases the export of complex products for both sides. Based on the results, stronger mutual trade relations increase the total gain not only for Turkey but for the CATRs, too. Lastly, in future studies, we plan to cover all Post-Soviet countries and reveal the relations between bilateral trade and the range of exported products

Texture based characterization of sub-skin features by specified laser speckle effects at λ=650nm region

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Objective: The textural structure of “skin age” related sub-skin components enables us to identify and analyse their unique characteristics, thus making substantial progress towards establishing an accurate skin age model. Methods: This is achieved by a two stage process. First by the application of textural analysis using laser speckle imaging, which is sensitive to textural effects within the λ=650 nm spectral band region. In the second stage a Bayesian inference method is used to select attributes from which a predictive model is built. Results: This technique enables us to contrast different skin age models, such as the laser-speckle effect against the more widely used normal light (LED) imaging method, whereby it is shown that our laser speckle based technique yields better results. Conclusion: The method introduced here is non-invasive, low-cost and capable of operating in real-time; having the potential to compete against high-cost instrumentation such as confocal microscopy or similar imaging devices used for skin age identification purposes

Imitation and Mirror Systems in Robots through Deep Modality Blending Networks

Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and imitation capabilities. This seems to be a strategy adopted by biological systems, in particular primates, as evidenced by the existence of mirror neurons that seem to be involved in multi-modal action understanding. How to benefit from the interaction experience of the robots to enable understanding actions and goals of other agents is still a challenging question. In this study, we propose a novel method, deep modality blending networks (DMBN), that creates a common latent space from multi-modal experience of a robot by blending multi-modal signals with a stochastic weighting mechanism. We show for the first time that deep learning, when combined with a novel modality blending scheme, can facilitate action recognition and produce structures to sustain anatomical and effect-based imitation capabilities. Our proposed system, can be conditioned on any desired sensory/motor value at any time-step, and can generate a complete multi-modal trajectory consistent with the desired conditioning in parallel avoiding accumulation of prediction errors. We further showed that given desired images from different perspectives, i.e. images generated by the observation of other robots placed on different sides of the table, our system could generate image and joint angle sequences that correspond to either anatomical or effect based imitation behavior. Overall, the proposed DMBN architecture not only serves as a computational model for sustaining mirror neuron-like capabilities, but also stands as a powerful machine learning architecture for high-dimensional multi-modal temporal data with robust retrieval capabilities operating with partial information in one or multiple modalities

Correction to: Accuracy and Prognostic Role of NCCT-ASPECTS Depend on Time from Acute Stroke Symptom-onset for both Human and Machine-learning Based Evaluation.

PURPOSE: We hypothesize that the detectability of early ischemic changes on non-contrast computed tomography (NCCT) is limited in hyperacute stroke for both human and machine-learning based evaluation. In short onset-time-to-imaging (OTI), the CT angiography collateral status may identify fast stroke progressors better than early ischemic changes quantified by ASPECTS. METHODS: In this retrospective, monocenter study, CT angiography collaterals (Tan score) and ASPECTS on acute and follow-up NCCT were evaluated by two raters. Additionally, a machine-learning algorithm evaluated the ASPECTS scale on the NCCT (e-ASPECTS). In this study 136 patients from 03/2015 to 12/2019 with occlusion of the main segment of the middle cerebral artery, with a defined symptom-onset-time and successful mechanical thrombectomy (MT) (modified treatment in cerebral infarction score mTICI = 2c or 3) were evaluated. RESULTS: Agreement between acute and follow-up ASPECTS were found to depend on OTI for both human (Intraclass correlation coefficient, ICC = 0.43 for OTI < 100 min, ICC = 0.57 for OTI 100–200 min, ICC = 0.81 for OTI ≥ 200 min) and machine-learning based ASPECTS evaluation (ICC = 0.24 for OTI < 100 min, ICC = 0.61 for OTI 100–200 min, ICC = 0.63 for OTI ≥ 200 min). The same applied to the interrater reliability. Collaterals were predictors of a favorable clinical outcome especially in hyperacute stroke with OTI < 100 min (collaterals: OR = 5.67 CI = 2.38–17.8, p < 0.001; ASPECTS: OR = 1.44, CI = 0.91–2.65, p = 0.15) while ASPECTS was in prolonged OTI ≥ 200 min (collaterals OR = 4.21,CI = 1.36–21.9, p = 0.03; ASPECTS: OR = 2.85, CI = 1.46–7.46, p = 0.01). CONCLUSION: The accuracy and reliability of NCCT-ASPECTS are time dependent for both human and machine-learning based evaluation, indicating reduced detectability of fast stroke progressors by NCCT. In hyperacute stroke, collateral status from CT-angiography may help for a better prognosis on clinical outcome and explain the occurrence of futile recanalization. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s00062-021-01110-5) contains supplementary material, which is available to authorized users

Accuracy and Prognostic Role of NCCT-ASPECTS Depend on Time from Acute Stroke Symptom-onset for both Human and Machine-learning Based Evaluation.

PURPOSE We hypothesize that the detectability of early ischemic changes on non-contrast computed tomography (NCCT) is limited in hyperacute stroke for both human and machine-learning based evaluation. In short onset-time-to-imaging (OTI), the CT angiography collateral status may identify fast stroke progressors better than early ischemic changes quantified by ASPECTS. METHODS In this retrospective, monocenter study, CT angiography collaterals (Tan score) and ASPECTS on acute and follow-up NCCT were evaluated by two raters. Additionally, a machine-learning algorithm evaluated the ASPECTS scale on the NCCT (e-ASPECTS). In this study 136 patients from 03/2015 to 12/2019 with occlusion of the main segment of the middle cerebral artery, with a defined symptom-onset-time and successful mechanical thrombectomy (MT) (modified treatment in cerebral infarction score mTICI = 2c or 3) were evaluated. RESULTS Agreement between acute and follow-up ASPECTS were found to depend on OTI for both human (Intraclass correlation coefficient, ICC = 0.43 for OTI < 100 min, ICC = 0.57 for OTI 100-200 min, ICC = 0.81 for OTI ≥ 200 min) and machine-learning based ASPECTS evaluation (ICC = 0.24 for OTI < 100 min, ICC = 0.61 for OTI 100-200 min, ICC = 0.63 for OTI ≥ 200 min). The same applied to the interrater reliability. Collaterals were predictors of a favorable clinical outcome especially in hyperacute stroke with OTI < 100 min (collaterals: OR = 5.67 CI = 2.38-17.8, p < 0.001; ASPECTS: OR = 1.44, CI = 0.91-2.65, p = 0.15) while ASPECTS was in prolonged OTI ≥ 200 min (collaterals OR = 4.21,CI = 1.36-21.9, p = 0.03; ASPECTS: OR = 2.85, CI = 1.46-7.46, p = 0.01). CONCLUSION The accuracy and reliability of NCCT-ASPECTS are time dependent for both human and machine-learning based evaluation, indicating reduced detectability of fast stroke progressors by NCCT. In hyperacute stroke, collateral status from CT-angiography may help for a better prognosis on clinical outcome and explain the occurrence of futile recanalization

Bio-nanohybrids of quantum dots and photoproteins facilitating strong nonradiative energy transfer

Cataloged from PDF version of article.Utilization of light is crucial for the life cycle of many organisms. Also, many organisms can create light by utilizing chemical energy emerged from biochemical reactions. Being the most important structural units of the organisms, proteins play a vital role in the formation of light in the form of bioluminescence. Such photoproteins have been isolated and identified for a long time; the exact mechanism of their bioluminescence is well established. Here we show a biomimetic approach to build a photoprotein based excitonic nanoassembly model system using colloidal quantum dots (QDs) for a new bioluminescent couple to be utilized in biotechnological and photonic applications. We concentrated on the formation mechanism of nanohybrids using a kinetic and thermodynamic approach. Finally we propose a biosensing scheme with an ON/OFF switch using the QD-GFP hybrid. The QD-GFP hybrid system promises strong exciton-exciton coupling between the protein and the quantum dot at a high efficiency level, possessing enhanced capabilities of light harvesting, which may bring new technological opportunities to mimic biophotonic events