Almost-zero-energy Eigenvalues of Some Broken Supersymmetric Systems

For a quantum mechanical system with broken supersymmetry, we present a simple method of determining the ground state when the corresponding energy eigenvalue is sufficiently small. A concise formula is derived for the approximate ground state energy in an associated, well-separated, asymmetric double-well-type potential. Our discussion is also relevant for the analysis of the fermion bound state in the kink-antikink scalar background.Comment: revised version, to be pubilshed in PR

Next to leading order non Fermi liquid corrections to the neutrino emissivity and cooling of the neutron star

In this work we derive the expressions of the neutrino mean free path(MFP) and emissivity with non Fermi liquid corrections up to next to leading order(NLO) in degenerate quark matter. The calculation has been performed both for the absorption and scattering processes. Subsequently the role of these NLO corrections on the cooling of the neutron star has been demonstrated. The cooling curve shows moderate enhancement compared to the leading order(LO) non-Fermi liquid result. Although the overall correction to the MFP and emissivity are larger compared to the free Fermi gas, the cooling behavior does not alter significantly.Comment: 8 pages, 8 figures, references added, matches published versio

Support for Wireless LAN Design

As mobile devices become common in mainstream computing, it becomes imperative to effectively design computing architectures that seamlessly and effectively integrate them. Rapid advancements in wireless technology has made it possible to build efficient wireless local area networks (WLANs). Designing WLANs presents some unique challenges. Some heuristics are available for WLAN design, but they represent piecemeal solutions, focusing on a limited set of issues. This paper provides a more comprehensive approach to WLAN design, by providing support for additional tasks in the design process, as well as by providing the designer the option of examining multiple competing options. The approach is developed in modular fashion, thereby permitting the easy substitution of alternative models in any phase of the process. We believe the approach to be useful for WLAN designers, and it provides an apt illustration of design science in information systems research

Smooth double barriers in quantum mechanics

Quantum mechanical tunneling across smooth double barrier potentials modeled using Gaussian functions, is analyzed numerically and by using the WKB approximation. The transmission probability, resonances as a function of incident particle energy, and their dependence on the barrier parameters are obtained for various cases. We also discuss the tunneling time, for which we obtain generalizations of the known results for rectangular barriers.Comment: 23 pages, 8 figures, a slightly reduced version to appear in American Journal of Physics, references correcte

Strong magnetic coupling between an electronic spin qubit and a mechanical resonator

We describe a technique that enables a strong, coherent coupling between a single electronic spin qubit associated with a nitrogen-vacancy impurity in diamond and the quantized motion of a magnetized nano-mechanical resonator tip. This coupling is achieved via careful preparation of dressed spin states which are highly sensitive to the motion of the resonator but insensitive to perturbations from the nuclear spin bath. In combination with optical pumping techniques, the coherent exchange between spin and motional excitations enables ground state cooling and the controlled generation of arbitrary quantum superpositions of resonator states. Optical spin readout techniques provide a general measurement toolbox for the resonator with quantum limited precision

Effect of Household detergents (Surfactants)Degraded through aquatic fungi

Commercial household detergents are diverse group of chemical that is best known for their wide used in laundry industries and household cleaning product. After use, residuals (Surfactant) detergents are discharge into sewage system directly or indirectly into the surface water and most of them end up dispersed into the different environment compartment of soil and water.”Water is facing lots of problems due to domestic waste. These toxic effects of surfactant damaging biodiversity of aquatic environment. Most of aquatic microorganism develops a bio-mechanism for degradation of harmful heavy metals discharge in water at high level. As we know that aquatic environment specially water “fungi” have excellent potential for metal accumulation, particularly genera of Rhizopus, Aspergillus, Streptoverticillum, Sacchromyctes. In general most of commercial household (Surfactants) detergents are biodegradable and amount of it can be commerciallyreduced by secondary treatment of Municipal sewage waste water plants. These discharge waste water pollutants with massive quantities especially surfactant could be a serious threat to aquatic ecosystem. Future studies of Commercial Surfactant toxicities and biodegradation are necessary to withdraw high toxic and non-biodegradableheavy metal for commercial use as a result makes an eco-friendly environment

PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network

We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN). Through PyCARL, we make the following two key contributions. First, we provide an interface of PyNN to CARLsim, a computationally-efficient, GPU-accelerated and biophysically-detailed SNN simulator. PyCARL facilitates joint development of machine learning models and code sharing between CARLsim and PyNN users, promoting an integrated and larger neuromorphic community. Second, we integrate cycle-accurate models of state-of-the-art neuromorphic hardware such as TrueNorth, Loihi, and DynapSE in PyCARL, to accurately model hardware latencies that delay spikes between communicating neurons and degrade performance. PyCARL allows users to analyze and optimize the performance difference between software-only simulation and hardware-software co-simulation of their machine learning models. We show that system designers can also use PyCARL to perform design-space exploration early in the product development stage, facilitating faster time-to-deployment of neuromorphic products. We evaluate the memory usage and simulation time of PyCARL using functionality tests, synthetic SNNs, and realistic applications. Our results demonstrate that for large SNNs, PyCARL does not lead to any significant overhead compared to CARLsim. We also use PyCARL to analyze these SNNs for a state-of-the-art neuromorphic hardware and demonstrate a significant performance deviation from software-only simulations. PyCARL allows to evaluate and minimize such differences early during model development.Comment: 10 pages, 25 figures. Accepted for publication at International Joint Conference on Neural Networks (IJCNN) 202