Mutual statistics, braid group, and the fractional quantum Hall effect

We show that the notion of mutual statistics arises naturally from the representation theory of the braid group over the multi-sheeted surface. A Hamiltonian which describes particles moving on the double-sheeted surface is proposed as a model for the bilayered fractional quantum Hall effect (FQHE) discovered recently. We explicitly show that the quasi-holes of the bilayered Hall fluid display fractional mutual statistics. A model for 3-dimensional FQHE using the multi-layered sample is suggested.Comment: LaTex 26 page

Quantum logic networks for probabilistic and controlled teleportation of unknown quantum states

We present simplification schemes for probabilistic and controlled teleportation of the unknown quantum states of both one-particle and two-particle and construct efficient quantum logic networks for implementing the new schemes by means of the primitive operations consisting of single-qubit gates, two-qubit controlled-not gates, Von Neumann measurement and classically controlled operations. In these schemes the teleportation are not always successful but with certain probability.Comment: 9 pages, 5 figure

Probabilistic Sea-Level Rise Hazard Analysis

This paper proposes a framework termed Probabilistic Sea-Level Rise Hazard Analysis (PSLRHA), to integrate the sea-level rise knowledge of current climate change scientific communities for informed engineering and policy decisions that affect coastal infrastructure, populations, and ecosystems. PSLRHA combines probabilities of all emission scenarios with predictions of the resulting sea-level rise over time, in order to compute sea-level rise hazard. PSLRHA also incorporates uncertainties in those sea-level rise predictions, by considering multiple Sea-Level Rise Prediction Models (SLRPMs). The output of the PSLRHA framework could be a Global Sea-Level Rise Hazard Map (GSLRHM) that can be used for Performance- Based Sea-Level Rise Engineering (PBSLRE)

Virtual Reality of Earthquake Ground Motions for Emergency Response

Ground motions interface earthquake science and engineering to advance understanding of seismic hazards and risk. Virtual reality provides an attractive tool to extend knowledge of the research community to a larger audience. This work visualizes emergency response under extreme motions, in the CAVE of the MARquette Visualization Laboratory. The visualization (a) displays ground motions (from the science community), (b) inputs these motions to structural models (from the engineering community) and illustrates the resulting responses, (c) translates structural responses to damage states of building elements, (d) creates a virtual room subjected to the perception associated with such earthquake shaking, and (e) introduces the human element of emergency response in this immersive environment. Building upon previous work on earthquake simulations, performance-based earthquake engineering (PBEE), building information modeling (BIM), and earthquake awareness, this study integrates elements of PBEE and BIM within the CAVE environment to provide visual information for decision making. Real-time or near real-time information via earthquake early warning (EEW) and structural health monitoring (SHM) further facilitates response within a limited time frame. As advanced technologies contribute to the future of community resilience, visualization plays an emerging role in connecting earthquake science, engineering, and policy

Adaptive Threshold Sampling and Estimation

Sampling is a fundamental problem in both computer science and statistics. A number of issues arise when designing a method based on sampling. These include statistical considerations such as constructing a good sampling design and ensuring there are good, tractable estimators for the quantities of interest as well as computational considerations such as designing fast algorithms for streaming data and ensuring the sample fits within memory constraints. Unfortunately, existing sampling methods are only able to address all of these issues in limited scenarios. We develop a framework that can be used to address these issues in a broad range of scenarios. In particular, it addresses the problem of drawing and using samples under some memory budget constraint. This problem can be challenging since the memory budget forces samples to be drawn non-independently and consequently, makes computation of resulting estimators difficult. At the core of the framework is the notion of a data adaptive thresholding scheme where the threshold effectively allows one to treat the non-independent sample as if it were drawn independently. We provide sufficient conditions for a thresholding scheme to allow this and provide ways to build and compose such schemes. Furthermore, we provide fast algorithms to efficiently sample under these thresholding schemes