Supervised Learning for Convolutional Neural Network with Barlow Twins

広島大学先進理工系科学研究科 2022年度 修士論

Animal models of anxiety: a review

Anxiety is a disorder that affects the quality of life and also imposes a huge economic burden. Animal models of anxiety have widened our understanding of the pathophysiology behind anxiety and to identify newer pharmacological compounds. Every behavioral test has its own limitations, and there are ways to minimize these like environment and handling. This review lists various experimental models of anxiety based on unlearned ethological models, learned responses like the Vogel conflict test, and psychological and physical stress models

Considerations for Real Time Data Analysis Using Multiple Magnetometer Sources for GIC Studies to Improve the Situational Awareness of an Electric Grid Model

This thesis aims to effectively utilize actual historical and real-time data to enhance electric grid model knowledge by reconstructing GMD scenarios. Magnetic and electric field data used for the analysis are generated and calculated from Texas A&M Magnetometer Network (TAMUMN). Days of GMD activity (G2, G1) during the past year are selected to reconstruct events with similar data on a synthetically developed version of the Texas electric grid with 7000 bus network. Upon integrating the data to a simulated power system model, the impact of geomagnetically induced currents (GIC) can be determined. By performing certain power system analysis techniques, the most affected regions, magnitudes of maximum current at substations and the transmission lines with the highest activity can be obtained. This is greatly useful for planning purposes and studies as it directs the user to focus on a specific section of the grid model and works toward strengthening it

Design of shark skin collagen-aloe composite scaffold for tissue engineering

Se ha demostrado que el colágeno es un nuevo biomaterial utilizado para la administración de fármacos, la fabricación de apósitos o como sustrato para ingeniería tisular cuya biocompatibilidad y propiedades biodegradables son únicas. El colágeno bovino y porcino tipo I constituyen una fuente fácilmente disponible de material de soporte para diversas aplicaciones biomédicas. Sin embargo, estas fuentes conllevan cierto riesgo potencial de enfermedades infecciosas como la encefalopatía espongiforme bovina o la encefalopatía espongiforme transmisible. Por esta razón, existe una demanda de colágeno tipo I procedente de otras fuentes. En el presente estudio, se utilizan animales acuáticos y, en concreto, especies de tiburón en las que el colágeno tipo I es una proteína principal de la piel y la estructura tiene similitud con la de las especies mamíferas. Se ha intentado utilizar colágeno de piel de tiburón como matriz de soporte con extracto de aloe para mejorar la estabilidad. Estas matrices de soporte se caracterizaron por varias propiedades fi sicoquímicas y por la evaluación de biocompatibilidad para facilitar el crecimiento de fi broblastos dérmicos humanos in vitro. La incorporación de extracto de aloe infl uyó enormemente en la morfología y las propiedades fi sicoquímicas de la matriz de soporte. Se observó in vitro que los fi broblastos conservaban la orientación organizada en forma de huso al cultivarse sobre la matriz de soporte de colágeno. Así, la matriz de soporte de colágeno desarrollada con una proporción de 10:1 de colágeno de piel de tiburón y extracto de aloe, respectivamente, sirvió como material biocompatible con una resistencia a la tracción apreciable. La investigación anterior sugiere que la matriz de soporte de colágeno de piel de tiburón desarrollada puede ser una alternativa efectiva al colágeno de mamífero en el campo de la ingeniería tisular y para diversas aplicaciones en la curación de heridas.Collagen has proven to be a novel biomaterial used for drug delivery, wound cover dressings or as a substrate for tissue engineering with unique biocompatibility and biodegradable properties. Bovine and porcine Type I collagen provide a readily available source of scaffold material for various biomedical applications. However these sources have some potential risk of infectious diseases such as bovine spongiform encephalopathy or transmissible spongiform encephalopathy. Hence there is demand for an alternative Type I collagen from various other sources. The present study utilizes the aquatic animals particularly the shark species in which collagen Type I is a major protein in the skin and the structure has similarity to that of mammalian species. An attempt was made to use shark skin collagen as scaffold with the extract of aloe to improve the stability. These scaffolds were characterized for various physicochemical properties and biocompatibility assessment to support the growth of human dermal fi broblasts in vitro. The incorporation of aloe extract highly infl uenced the morphology and physicochemical properties of the scaffold. It was observed in vitro that the fi broblasts retained the spindle shape, organized orientation when cultured over collagen scaffold. Thus the developed collagen scaffold at 10: 1 ratio of shark skin collagen and aloe extract respectively served as a biocompatible material with appreciable tensile strength. The above investigation suggests that the developed shark skin collagen scaffold could be an effective alternative for the mammalian collagen for tissue engineering and various wound healing applications

Implicit complexity for coinductive data: a characterization of corecurrence

We propose a framework for reasoning about programs that manipulate coinductive data as well as inductive data. Our approach is based on using equational programs, which support a seamless combination of computation and reasoning, and using productivity (fairness) as the fundamental assertion, rather than bi-simulation. The latter is expressible in terms of the former. As an application to this framework, we give an implicit characterization of corecurrence: a function is definable using corecurrence iff its productivity is provable using coinduction for formulas in which data-predicates do not occur negatively. This is an analog, albeit in weaker form, of a characterization of recurrence (i.e. primitive recursion) in [Leivant, Unipolar induction, TCS 318, 2004].Comment: In Proceedings DICE 2011, arXiv:1201.034

Characterizing polynomial time complexity of stream programs using interpretations

This paper provides a criterion based on interpretation methods on term rewrite systems in order to characterize the polynomial time complexity of second order functionals. For that purpose it introduces a first order functional stream language that allows the programmer to implement second order functionals. This characterization is extended through the use of exp-poly interpretations as an attempt to capture the class of Basic Feasible Functionals (bff). Moreover, these results are adapted to provide a new characterization of polynomial time complexity in computable analysis. These characterizations give a new insight on the relations between the complexity of functional stream programs and the classes of functions computed by Oracle Turing Machine, where oracles are treated as inputs

A score based malware classification approach for mobile forensic analysis

The rapid growth of Android as one of the leading Operating System (OS) for mobile devices drives the need of effective security measures to ensure the users have a safer platform to use. Boolean based features used for application permissions degrades the precision, recall, F-1 score and accuracy of malware detection. The reason for this is that Boolean based features classify the benign and malware applications based on true or false rule which is done based on the binary 0 for benign and 1 for malware. FAMOUS (Forensic Analysis of MObile devices Using Scoring of application permissions) which incorporates Effective Maliciousness Score of Permission (EMSP), a score based representation for permissions which replaces the Boolean representation for permissions have produced better result for the accuracy, precision, recall and F1-score over the Boolean based feature from existing works. FAMOUS is tested on the crawled datasets that are collected from multiple public archives such as Cantagio dump, AndroMalShare, Derbin project and Andrototal. This crawled datasets are then labelled by the result captured from Virus Total engines. Thus, FAMOUS did not use any standard dataset for its analysis. In his research, we will implement the EMSP, a score based triage and test it over Android Malware Dataset (AMD) and Android PRAGuard dataset to ensure reliable result obtained for the Accuracy, Precision, Recall and F1-Score through Machine Learning classifiers. Total of five classifiers have been used to train and test the datasets which consist of Random Forest, Decision Tree, Naive Bayes, K-nearest neighbours, and Support Vector Machine. EMSP will be implemented using Python programming language on Windows system. The performance metrics evaluated for the research are precision, recall, F-1 score and accuracy. The accuracy obtained varies for different classifiers for AMD and Android PRAGuard dataset. The best result obtained for Random Forest classifier when using AMD