Necessary and sufficient conditions for consistent root reconstruction in Markov models on trees

We establish necessary and sufficient conditions for consistent root reconstruction in continuous-time Markov models with countable state space on bounded-height trees. Here a root state estimator is said to be consistent if the probability that it returns to the true root state converges to 1 as the number of leaves tends to infinity. We also derive quantitative bounds on the error of reconstruction. Our results answer a question of Gascuel and Steel and have implications for ancestral sequence reconstruction in a classical evolutionary model of nucleotide insertion and deletion.Comment: 30 pages, 3 figures, title of reference [FR] is update

Micro-Deformable Mirror: a low cost technology for cryogenic active and adaptive optics

Electrostatically operated bi-directional deflecting silicon membranes can be created using bulk micromachining techniques. The 19-channel Micro-Deformable Mirror, potential candidate for space Active and Adaptive Optics, was built by OKO Technologies. The purpose of this project was to test the mirror, constituted from its silicon membrane and its aluminium coated mount, under cryogenic conditions. The mirror was mounted on a self designed three piece structure and several experiments were constructed to characterise the zero point drift, the maximum stroke and the dynamical response of the MDM. The membrane was tested by interferometry and showed stability within 0.34μm of the initial membrane figure (flat within (^λ)(_13), λ = 0.633μm, at room temperature). Space temperatures are down to 35K and the MDM showed great dynamical behaviour at temperatures down to 86K. Adaptive Optics require a frequency response of the order of 1kHz. C++ programs drove the MDM to frequencies of up to 1.66kHz. As it can work under cryogenic conditions and has high frequency response, the MDM leads to great expectations for inexpensive wavefront correction at infrared wavelengths

Out With the Old, In With the New: Education in the Hospitality Industry

What does it mean to be Chief Executive Officer (CEO) of the largest hotel group in the world outside of the United States? How do you effectively lead a company that manages so many different brands and franchises? And how is it best to train new employees to be part of that shared vision? Accor is one of the largest hospitality companies in the world, with resorts, hotels and vacation properties across the globe. current CEO Sebastien Bazin is hoping to bring together the world of hospitality and education to build a bright and sustainable future for the company. Rosen College Ph.D. student Louis A. Lenglet caught up with Bazin to find out how

A high bandwidth quantum repeater

We present a physical- and link-level design for the creation of entangled pairs to be used in quantum repeater applications where one can control the noise level of the initially distributed pairs. The system can tune dynamically, trading initial fidelity for success probability, from high fidelity pairs (F=0.98 or above) to moderate fidelity pairs. The same physical resources that create the long-distance entanglement are used to implement the local gates required for entanglement purification and swapping, creating a homogeneous repeater architecture. Optimizing the noise properties of the initially distributed pairs significantly improves the rate of generating long-distance Bell pairs. Finally, we discuss the performance trade-off between spatial and temporal resources.Comment: 5 page

Weak non-linearities and cluster states

We propose a scalable approach to building cluster states of matter qubits using coherent states of light. Recent work on the subject relies on the use of single photonic qubits in the measurement process. These schemes have a low initial success probability and low detector efficiencies cause a serious blowup in resources. In contrast, our approach uses continuous variables and highly efficient measurements. We present a two-qubit scheme, with a simple homodyne measurement system yielding an entangling operation with success probability 1/2. Then we extend this to a three-qubit interaction, increasing this probability to 3/4. We discuss the important issues of the overhead cost and the time scaling, showing how these can be vastly improved with access to this new probability range.Comment: 5 pages, to appear in Phys. Rev.

Heap Models, Composition and Control

International audienc

The resilience of verbal sequence learning:Evidence from the Hebb repetition effect

In a single large-scale study, we demonstrate that verbal sequence learning as studied using the classic Hebb repetition effect (Hebb, 1961)—the improvement in the serial recall of a repeating sequence compared to non-repeated sequences—is resilient to both wide and irregular spacing between sequence repetitions. Learning of a repeated sequence of letters was evident to a comparable degree with three, five, and eight intervening non-repeated sequences and regardless of whether the spacing between repetitions was regular or irregular. Importantly, this resilience of verbal sequence learning was observed despite the fact that there was complete item-set overlap between repeated and non-repeated sequences. The findings are consistent with the conceptualization of the Hebb repetition effect as a laboratory analogue of natural phonological word-form learning. The results also have implications for the two leading models of Hebb sequence learning: Whereas the results are incompatible with the model of Page and Norris (2009), they can be handled readily by the model of Burgess and Hitch (2006) through the abandonment of its assumption of long-term (across-trial level) decay

Efficient Identification of Assembly Neurons within Massively Parallel Spike Trains

The chance of detecting assembly activity is expected to increase if the spiking activities of large numbers of neurons are recorded simultaneously. Although such massively parallel recordings are now becoming available, methods able to analyze such data for spike correlation are still rare, as a combinatorial explosion often makes it infeasible to extend methods developed for smaller data sets. By evaluating pattern complexity distributions the existence of correlated groups can be detected, but their member neurons cannot be identified. In this contribution, we present approaches to actually identify the individual neurons involved in assemblies. Our results may complement other methods and also provide a way to reduce data sets to the “relevant” neurons, thus allowing us to carry out a refined analysis of the detailed correlation structure due to reduced computation time