Numerical Renormalization Group Study of the O(3)-symmetric Anderson Model

We use the numerical renormalization group method to study the O(3)-symmetric version of the impurity Anderson model of Coleman and Schofield. This model is of general interest because it displays both Fermi liquid and non-Fermi liquid behaviour, and in the large $U$ limit can be related to the compactified two channel Kondo model of Coleman, Ioffe and Tsvelik. We calculate the thermodynamics for a parameter range which covers the full range of behaviour of the model. We find a non-Fermi liquid fixed point in the isotropic case which is unstable with respect to channel anisotropy.Comment: 10 pages, LaTeX, 8 figures includes as eps-file

An Investigation on the Optimisation Technique of Graphene Oxide Synthesis for Biomedical Applications

Graphene Oxide (GO) demonstrates various properties suitable for a plethora of applications. Till now the most common method for GO synthesis has been Hummers method. Yet this method has many disadvantages like risk of explosion, inability to scale up etc. Hence, here we have used Marcano’s synthesis method with few modifications such as reduction in initial quantity of graphite, duration of heat treatment and decreasing the number of filtration steps. Further, we analysed the effect of a range of graphite concentrations as the reactant on the yield of GO synthesized. We characterized the GO by UV-Vis spectrophotometry, Field Emission Scanning Electron Microscopy (FESEM) analysis, X-Ray Diffraction and Raman Spectroscopy techniques. X-Ray Diffraction and Raman analyzes identified the nanostructured GO. UV-Vis Spectroscopy confirmed the formation of GO by showing the characteristic peak close to 230nm. FESEM imaging showed the formation of both two-dimensional and three-dimensional GO nanostructures. Suitability for biomedical applications of the synthesized GO was also analyzed by experiments such as hemolytic assay and its effects on bacterial growth. It was found that at higher concentration of GO, hemolysis was induced in a dose-dependent manner and it was within 10% as compared to Triton-X detergent. Moreover, GO was found to enhance the growth of both E.coli and P. aeruginosa in a concentration-dependent manner

Asymptotic analysis and spectrum of three anyons

The spectrum of anyons confined in harmonic oscillator potential shows both linear and nonlinear dependence on the statistical parameter. While the existence of exact linear solutions have been shown analytically, the nonlinear dependence has been arrived at by numerical and/or perturbative methods. We develop a method which shows the possibility of nonlinearly interpolating spectrum. To be specific we analyse the eigenvalue equation in various asymptotic regions for the three anyon problem.Comment: 28 pages, LaTeX, 2 Figure

eLearning

E-Learning Management System is one of the best systems for learning. During covid-19 period all students and teachers had struggled to face teach / learn. Purpose of eLearning is the easiest way to learn or instruct from our convenient place. Maybe if the instructor / learner do not have time at that particular time they are advising / learning leisure time. Instructor / Learner can teach / learn from one country to another. Anyone can learn / instruct from anywhere using this Management system. It’s not necessary to teach them from on-site / online. If the instructor records the flow and uploads their course materials, they will see and learn everything. This application has three roles one is an Instructor, Learner and Admin. Instructors who upload the course material / content for the learner’s study material. Admin can manage everything like instructors, users, categories, plan details. Learner can register their account, raise the doubts using forum and will receive the notifications from admin/instructors. To build the application handling the above features, we are using the logic for .NET Core on Visual studio. Besides, we are using the web technologies like HTML5, CSS3, Bootstrap, Ajax, Asp.net, and jQuery to develop and support the application, and for the database, MSSQL will be used

Differentiable world programs

L'intelligence artificielle (IA) moderne a ouvert de nouvelles perspectives prometteuses pour la création de robots intelligents. En particulier, les architectures d'apprentissage basées sur le gradient (réseaux neuronaux profonds) ont considérablement amélioré la compréhension des scènes 3D en termes de perception, de raisonnement et d'action. Cependant, ces progrès ont affaibli l'attrait de nombreuses techniques ``classiques'' développées au cours des dernières décennies. Nous postulons qu'un mélange de méthodes ``classiques'' et ``apprises'' est la voie la plus prometteuse pour développer des modèles du monde flexibles, interprétables et exploitables : une nécessité pour les agents intelligents incorporés. La question centrale de cette thèse est : ``Quelle est la manière idéale de combiner les techniques classiques avec des architectures d'apprentissage basées sur le gradient pour une compréhension riche du monde 3D ?''. Cette vision ouvre la voie à une multitude d'applications qui ont un impact fondamental sur la façon dont les agents physiques perçoivent et interagissent avec leur environnement. Cette thèse, appelée ``programmes différentiables pour modèler l'environnement'', unifie les efforts de plusieurs domaines étroitement liés mais actuellement disjoints, notamment la robotique, la vision par ordinateur, l'infographie et l'IA. Ma première contribution---gradSLAM--- est un système de localisation et de cartographie simultanées (SLAM) dense et entièrement différentiable. En permettant le calcul du gradient à travers des composants autrement non différentiables tels que l'optimisation non linéaire par moindres carrés, le raycasting, l'odométrie visuelle et la cartographie dense, gradSLAM ouvre de nouvelles voies pour intégrer la reconstruction 3D classique et l'apprentissage profond. Ma deuxième contribution - taskography - propose une sparsification conditionnée par la tâche de grandes scènes 3D encodées sous forme de graphes de scènes 3D. Cela permet aux planificateurs classiques d'égaler (et de surpasser) les planificateurs de pointe basés sur l'apprentissage en concentrant le calcul sur les attributs de la scène pertinents pour la tâche. Ma troisième et dernière contribution---gradSim--- est un simulateur entièrement différentiable qui combine des moteurs physiques et graphiques différentiables pour permettre l'estimation des paramètres physiques et le contrôle visuomoteur, uniquement à partir de vidéos ou d'une image fixe.Modern artificial intelligence (AI) has created exciting new opportunities for building intelligent robots. In particular, gradient-based learning architectures (deep neural networks) have tremendously improved 3D scene understanding in terms of perception, reasoning, and action. However, these advancements have undermined many ``classical'' techniques developed over the last few decades. We postulate that a blend of ``classical'' and ``learned'' methods is the most promising path to developing flexible, interpretable, and actionable models of the world: a necessity for intelligent embodied agents. ``What is the ideal way to combine classical techniques with gradient-based learning architectures for a rich understanding of the 3D world?'' is the central question in this dissertation. This understanding enables a multitude of applications that fundamentally impact how embodied agents perceive and interact with their environment. This dissertation, dubbed ``differentiable world programs'', unifies efforts from multiple closely-related but currently-disjoint fields including robotics, computer vision, computer graphics, and AI. Our first contribution---gradSLAM---is a fully differentiable dense simultaneous localization and mapping (SLAM) system. By enabling gradient computation through otherwise non-differentiable components such as nonlinear least squares optimization, ray casting, visual odometry, and dense mapping, gradSLAM opens up new avenues for integrating classical 3D reconstruction and deep learning. Our second contribution---taskography---proposes a task-conditioned sparsification of large 3D scenes encoded as 3D scene graphs. This enables classical planners to match (and surpass) state-of-the-art learning-based planners by focusing computation on task-relevant scene attributes. Our third and final contribution---gradSim---is a fully differentiable simulator that composes differentiable physics and graphics engines to enable physical parameter estimation and visuomotor control, solely from videos or a still image