Exploring Quantum Neural Networks for the Discovery and Implementation of Quantum Error-Correcting Codes

We investigate the use of Quantum Neural Networks for discovering and implementing quantum error-correcting codes. Our research showcases the efficacy of Quantum Neural Networks through the successful implementation of the Bit-Flip quantum error-correcting code using a Quantum Autoencoder, effectively correcting bit-flip errors in arbitrary logical qubit states. Additionally, we employ Quantum Neural Networks to restore states impacted by Amplitude Damping by utilizing an approximative 4-qubit error-correcting codeword. Our models required modification to the initially proposed Quantum Neural Network structure to avoid barren plateaus of the cost function and improve training time. Moreover, we propose a strategy that leverages Quantum Neural Networks to discover new encryption protocols tailored for specific quantum channels. This is exemplified by learning to generate logical qubits explicitly for the bit-flip channel. Our modified Quantum Neural Networks consistently outperformed the standard implementations across all tasks

Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia

Neurodevelopmental processes are widely believed to underlie schizophrenia. Analysis of brain texture from conventional magnetic resonance imaging (MRI) can detect disturbance in brain cytoarchitecture. We tested the hypothesis that patients with schizophrenia manifest quantitative differences in brain texture that, alongside discrete volumetric changes, may serve as an endophenotypic biomarker. Texture analysis (TA) of grey matter distribution and voxel-based morphometry (VBM) of regional brain volumes were applied to MRI scans of 27 patients with schizophrenia and 24 controls. Texture parameters (uniformity and entropy) were also used as covariates in VBM analyses to test for correspondence with regional brain volume. Linear discriminant analysis tested if texture and volumetric data predicted diagnostic group membership (schizophrenia or control). We found that uniformity and entropy of grey matter differed significantly between individuals with schizophrenia and controls at the fine spatial scale (filter width below 2 mm). Within the schizophrenia group, these texture parameters correlated with volumes of the left hippocampus, right amygdala and cerebellum. The best predictor of diagnostic group membership was the combination of fine texture heterogeneity and left hippocampal size. This study highlights the presence of distributed grey-matter abnormalities in schizophrenia, and their relation to focal structural abnormality of the hippocampus. The conjunction of these features has potential as a neuroimaging endophenotype of schizophrenia

Abnormalities in fronto-striatal connectivity within language networks relate to differences in grey-matter heterogeneity in Asperger syndrome

Abstract Asperger syndrome (AS) is an Autism Spectrum Disorder (ASD) characterised by qualitative impairment in the development of emotional and social skills with relative preservation of general intellectual abilities, including verbal language. People with AS may nevertheless show atypical language, including rate and frequency of speech production. We previously observed that abnormalities in grey matter homogeneity (measured with texture analysis of structural MR images) in AS individuals when compared with controls are also correlated with the volume of caudate nucleus. Here, we tested a prediction that these distributed abnormalities in grey matter compromise the functional integrity of brain networks supporting verbal communication skills. We therefore measured the functional connectivity between caudate nucleus and cortex during a functional neuroimaging study of language generation (verbal fluency), applying psycho-physiological interaction (PPI) methods to test specifically for differences attributable to grey matter heterogeneity in AS participants. Furthermore, we used dynamic causal modelling (DCM) to characterise the causal directionality of these differences in interregional connectivity during word production. Our results revealed a diagnosis-dependent influence of grey matter heterogeneity on the functional connectivity of the caudate nuclei with right insula/inferior frontal gyrus and anterior cingulate, respectively with the left superior frontal gyrus and right precuneus. Moreover, causal modelling of interactions between inferior frontal gyri, caudate and precuneus, revealed a reliance on bottom-up (stimulus-driven) connections in AS participants that contrasted with a dominance of top-down (cognitive control) connections from prefrontal cortex observed in control participants. These results provide detailed support for previously hypothesised central disconnectivity in ASD and specify discrete brain network targets for diagnosis and therapy in ASD

An investigation of the relationship between responsibility and pay

SIGLEAvailable from British Library Document Supply Centre- DSC:D061482 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Διαφοροποίηση σκλήρυνσης κατά πλάκας από εγκεφαλική μικροαγγειοπάθεια με σύγχρονες μεθόδους αναγνώρισης προτύπων σε εικόνες μαγνητικού συντονισμού

The diagnosis of Multiple Sclerosis (MS) is primarily based on the clinical examination, while it is supported by Magnetic Resonance (MR) imaging evaluated by experienced radiologists. Although, the typical imaging characteristics of MS follow well documented patterns, there are other pathologies affecting the Central Nervous System (CNS) that resemble the imaging characteristics of MS and vice versa. Cerebral Microangiopathy (CM) belongs to such pathologies that may puzzle the radiologist regarding his/her final decision. The differential diagnosis problem usually arises at the onset of the disease, when there is no spread of the signs and symptoms in space and time. The early diagnosis of both diseases is of great importance for the beginning of the right treatment. The aim of the present thesis was to evaluate whether textural features may help in discriminating MS from CM. This was achieved by designing, implementing, and evaluating a pattern recognition system on MR images employing textural features. The clinical material consisted of 29 patients all scanned with the same MR protocol. The MS group comprised of 11 patients diagnosed with clinically definite MS. On the other hand, the CM group included 18 patients with verified CM. Every patient was scanned on a MAGNETOM Sonata MR modality of 1.5 Tesla with the Fluid Attenuated Inversion Recovery (FLAIR) protocol at the 251 General Airforce Hospital. Twenty-three textural features were calculated, 4 from the image histogram, 14 from the co-occurrence matrices and 5 from the run-length matrices. The regions used included MS and CM lesions in addition to the Normal Appearing White Matter (NAWM) adjacent to each lesion. The classification methods utilized in the present thesis included a/ the Probabilistic Neural Network (PNN) classifier used to estimate the capability of textural features in discriminating MS from CM and b/ the combination of the PNN classifier, the Support Vector Machines (SVM) classifier and the k-Nearest Neighbor classifier (k-NN) evaluated each one separately and as a whole in a Multi-Classifier (MC) system. Additionally, the Least-Square Feature Transformation (LSFT) technique was applied to improve the accuracy of the classification system by clustering the textural features, for each pathology, around arbitrary pre-selected points rendering them more separable. The performance evaluation of the designed classification schemes was based on the External Cross Validation (ECV) process, which is considered indicative for the generalization of the designed classification system to ‘unseen’ cases. It was found that the textural features calculated from MS and CM lesions contain useful clinical information regarding the texture of MS and CM as depicted on MR images. The classification accuracy attained was 73% in correctly discriminating MS from CM utilizing the ECV method. In addition, the utilization of the adjacent NAWM to each lesion and the LSFT technique in the classification scheme boosted the classification accuracy by 10% resulting in 83% overall classification accuracy in the MC system. The textural features that participated in the optimum feature vector were related to the degree of homogeneity, the amount of randomness and the dispersion of the gray-tone intensity values within the texture of the MS and CM. These textural characteristics are related to textural parameters that physicians employ in diagnosis and they were proportional to the textural imprint of MS and CM lesions i.e. MS-regions were darker, of higher contrast, less homogeneous, and rougher as compared to CM. Finally, the proposed system might be of value as an assisting tool in lesion characterization when MS differential diagnosis issues arise.Εισαγωγή: Η σωστή διάγνωση της Σκλήρυνσης κατά Πλάκας (ΣκΠ) βασίζεται πρωτίστως στην αναλυτική παρατήρηση κλινικών συμπτωμάτων καθώς επίσης υποβοηθείται από την αξιολόγηση εικόνων Μαγνητικού Συντονισμού (ΜΣ) από έμπειρους ακτινολόγους. Αν και τα τυπικά απεικονιστικά χαρακτηριστικά της ΣκΠ είναι καταγεγραμμένα λεπτομερώς στην παγκόσμια ιατρική βιβλιογραφία[1], υπάρχουν άλλες παθολογίες του Κεντρικού Νευρικού Συστήματος των οποίων τα απεικονιστικά χαρακτηριστικά προσομοιάζουν εκείνα της ΣκΠ[2]. Σε αυτές τις περιπτώσεις, ιδίως στην αρχική εμφάνιση της ασθένειας, προβλήματα διαφορικής διάγνωσης μπορούν να προκύψουν. Μια από τις πιο συχνά εμφανιζόμενες παθολογίες διαφορικής διάγνωσης είναι η Εγκεφαλική Μικροαγγειοπάθεια (ΕΜ)[3]. Η σωστή και έγκαιρη διάγνωση της ΣκΠ είναι πολύ σημαντική για την πορεία της ασθένειας επηρεάζοντας σαφώς και την ποιότητα ζωής του ασθενούς. Ο αυτόματος διαχωρισμός ΣκΠ από ΕΜ με την χρήση χαρακτηριστικών υφής από εικόνες ΜΣ δεν έχει εφαρμοστεί προηγουμένως. Γενικότερα η βιβλιογραφία πάνω στην διερεύνηση του προβλήματος της διαφορικής διάγνωσης της ΣκΠ από άλλες παθολογίες είναι περιορισμένη. Στόχοι της παρούσας διατριβής: Οι στόχοι της παρούσας διδακτορικής διατριβής συνοψίζονται στα παρακάτω: • Ο σχεδιασμός, η υλοποίηση και η αξιολόγηση ενός συστήματος αναγνώρισης προτύπων για την διαφοροποίηση της ΣκΠ από την ΕΜ. Ο στόχος επιτεύχθηκε αναλύοντας χαρακτηριστικά υφής υπολογισμένα από εικόνες ΜΣ. Ο πρωταρχικός στόχος ήταν να γίνει μια πρωτογενής αξιολόγηση των χαρακτηριστικών υφής για το κατά πόσο παρέχουν πληροφορία ικανή να διαφοροποιήσει την ΣκΠ από την ΕΜ. Εν κατακλείδι, δείχθηκε ότι με την χρήση Πιθανοκρατικού Νευρωνικού Δικτύου (ΠΝΔ)[4] μπορούμε να έχουμε διαφοροποίηση με 73% ποσοστό επιτυχία. • Διερεύνηση της συνεισφοράς της Φαινομενικά Υγιούς Λευκής Ουσίας (ΦΥΛΟ) αξιολογώντας τα χαρακτηριστικά υφής της. Πρωτίστως τα χαρακτηριστικά υφής αξιολογήθηκαν μεμονωμένα εξετάζοντας την ακρίβεια της ΦΥΛΟ γύρω από αλλοιώσεις ΣκΠ έναντι ΦΥΛΟ προερχόμενες γύρω από αλλοιώσεις ΕΜ. Επιπροσθέτως, αξιολογήθηκε η ακρίβεια του συστήματος σε χαρακτηριστικά προερχόμενα από τον συνδυασμό χαρακτηριστικών υφής από αλλοιώσεις και από τις ΦΥΛΟ περιοχές. • Τέλος, ένα σύστημα πολλαπλών ταξινομητών δημιουργήθηκε και αξιολογήθηκε. Τρείς ευρέως χρησιμοποιούμενοι ταξινομητές χρησιμοποιήθηκαν. Εφαρμόζοντας μετασχηματισμό ελαχίστων τετραγώνων πάνω στα χαρακτηριστικά, καταφέραμε να αυξήσουμε την ακρίβεια του συστήματος κατά 10% φτάνοντας στην ολική ακρίβεια του 83%. […