The Capabilities of the upgraded MIPP experiment with respect to Hypernuclear physics

We describe the state of analysis of the MIPP experiment, its plans to upgrade the experiment and the impact such an upgraded experiment will have on hypernuclear physics

Status of Neutrino Factory R&D within the Muon Collaboration

We describe the current status of the research within the Muon Collaboration towards realizing a Neutrino Factory. We describe briefly the physics motivation behind the neutrino factory approach to studying neutrino oscillations and the longer term goal of building the Muon Collider. The benefits of a step by step staged approach of building a proton driver, collecting and cooling muons followed by the acceleration and storage of cooled muons are emphasized. Several usages of cooled muons open up at each new stage in such an approach and new physics opportunites are realized at the completion of each stage.Comment: 19 pages, 20 figures. To Appear in the Proceedings of the International Workshop on Neutrino Oscillations in Venice, NO-VE 200

A neural circuit for navigation inspired by C. elegans Chemotaxis

We develop an artificial neural circuit for contour tracking and navigation inspired by the chemotaxis of the nematode Caenorhabditis elegans. In order to harness the computational advantages spiking neural networks promise over their non-spiking counterparts, we develop a network comprising 7-spiking neurons with non-plastic synapses which we show is extremely robust in tracking a range of concentrations. Our worm uses information regarding local temporal gradients in sodium chloride concentration to decide the instantaneous path for foraging, exploration and tracking. A key neuron pair in the C. elegans chemotaxis network is the ASEL & ASER neuron pair, which capture the gradient of concentration sensed by the worm in their graded membrane potentials. The primary sensory neurons for our network are a pair of artificial spiking neurons that function as gradient detectors whose design is adapted from a computational model of the ASE neuron pair in C. elegans. Simulations show that our worm is able to detect the set-point with approximately four times higher probability than the optimal memoryless Levy foraging model. We also show that our spiking neural network is much more efficient and noise-resilient while navigating and tracking a contour, as compared to an equivalent non-spiking network. We demonstrate that our model is extremely robust to noise and with slight modifications can be used for other practical applications such as obstacle avoidance. Our network model could also be extended for use in three-dimensional contour tracking or obstacle avoidance

Isolated Singularities of Polyharmonic Operator in Even Dimension

We consider the equation $\Delta^2 u=g(x,u) \geq 0$ in the sense of distribution in $\Omega'=\Omega\setminus \{0\}$ where $u$ and $-\Delta u\geq 0.$ Then it is known that $u$ solves $\Delta^2 u=g(x,u)+\alpha \delta_0-\beta \Delta \delta_0,$ for some non-negative constants $\alpha$ and $\beta.$ In this paper we study the existence of singular solutions to $\Delta^2 u= a(x) f(u)+\alpha \delta_0-\beta \Delta \delta_0$ in a domain $\Omega\subset \mathbb{R}^4,$ $a$ is a non-negative measurable function in some Lebesgue space. If $\Delta^2 u=a(x)f(u)$ in $\Omega',$ then we find the growth of the nonlinearity $f$ that determines $\alpha$ and $\beta$ to be $0.$ In case when $\alpha=\beta =0,$ we will establish regularity results when $f(t)\leq C e^{\gamma t},$ for some $C, \gamma>0.$ This paper extends the work of Soranzo (1997) where the author finds the barrier function in higher dimensions $(N\geq 5)$ with a specific weight function $a(x)=|x|^\sigma.$ Later we discuss its analogous generalization for the polyharmonic operator