Implementation of Banker’s Algorithm Using Dynamic Modified Approach

Banker’s algorithm referred to as resource allocation and deadlock avoidance algorithm that checks for the safety by simulating the allocation of predetermined maximum possible of resources and makes the system into s-state by checking the possible deadlock conditions for all other pending processes. It needs to know how much of each resource a process could possibly request. Number of processes are static in algorithm, but in most of system processes varies dynamically and no additional process will be started while it is in execution. The number of resources are not allow to go down while it is in execution. In this research an approach for Dynamic Banker's algorithm is proposed which allows the number of resources to be changed at runtime that prevents the system to fall in unsafe state. It also give details about all the resources and processes that which one require resources and in what quantity. This also allocates the resource automatically to the stopped process for the execution and will always give the appropriate safe sequence for the given processes

Numerical consistency check between two approaches to radiative corrections for neutrino masses and mixings

We briefly outline the two popular approaches on radiative corrections to neutrino masses and mixing angles, and then carry out a detailed numerical analysis for a consistency check between them in MSSM. We find that the two approaches are nearly consistent with a small discrepancy of a factor of 13 percent in mass eigenvalues at low energy scale, but the predictions on mixing angles are almost consistent. We check the stability of the three types of neutrino models, i.e., hierarchical, inverted hierarchical and degenerate models, under radiative corrections, using both approaches, and find consistent conclusions. The neutrino mass models which are found to be stable under radiative corrections in MSSM are the normal hierarchical model and the inverted hierarchical model with opposite CP parity. We also carry out numerical analysis on some important conjectures related to radiative corrections in MSSM, viz., radiative magnification of solar and atmospheric mixings in case of nearly degenerate model having same CP parity (MPR conjecture) and radiative generation of solar mass scale in exactly two-fold degenerate model with opposite CP parity and non-zero reactor angle (JM conjecture). We observe certain exceptions to these conjectures. Finally the effect of scale-dependent vacuum expectation value in neutrino mass renormalisation is discussed.Comment: 26 pages, 5 figures,references added, typos corrected and text modifie

Discriminating neutrino mass models using Type II seesaw formula

In this paper we propose a kind of natural selection which can discriminate the three possible neutrino mass models, namely the degenerate, inverted hierarchical and normal hierarchical models, using the framework of Type II seesaw formula. We arrive at a conclusion that the inverted hierarchical model appears to be most favourable whereas the normal hierarchical model follows next to it. The degenerate model is found to be most unfavourable. We use the hypothesis that those neutrino mass models in which Type I seesaw term dominates over the Type II left-handed Higgs triplet term are favoured to survive in nature.Comment: No change in the results, a few references added, some changes in Type[IIB] calculation

Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots

We present Habitat 3.0: a simulation platform for studying collaborative human-robot tasks in home environments. Habitat 3.0 offers contributions across three dimensions: (1) Accurate humanoid simulation: addressing challenges in modeling complex deformable bodies and diversity in appearance and motion, all while ensuring high simulation speed. (2) Human-in-the-loop infrastructure: enabling real human interaction with simulated robots via mouse/keyboard or a VR interface, facilitating evaluation of robot policies with human input. (3) Collaborative tasks: studying two collaborative tasks, Social Navigation and Social Rearrangement. Social Navigation investigates a robot's ability to locate and follow humanoid avatars in unseen environments, whereas Social Rearrangement addresses collaboration between a humanoid and robot while rearranging a scene. These contributions allow us to study end-to-end learned and heuristic baselines for human-robot collaboration in-depth, as well as evaluate them with humans in the loop. Our experiments demonstrate that learned robot policies lead to efficient task completion when collaborating with unseen humanoid agents and human partners that might exhibit behaviors that the robot has not seen before. Additionally, we observe emergent behaviors during collaborative task execution, such as the robot yielding space when obstructing a humanoid agent, thereby allowing the effective completion of the task by the humanoid agent. Furthermore, our experiments using the human-in-the-loop tool demonstrate that our automated evaluation with humanoids can provide an indication of the relative ordering of different policies when evaluated with real human collaborators. Habitat 3.0 unlocks interesting new features in simulators for Embodied AI, and we hope it paves the way for a new frontier of embodied human-AI interaction capabilities.Comment: Project page: http://aihabitat.org/habitat