Reclaimed Identity/Innov - Roc: the Innovation Hub + Revitalization of High Falls, Rochester, NY

This project is not about creating a piece of architecture that will serve as an icon to an idea or movement. Rather, this project is about how a series of smaller architectural interventions can reinforce a context that is already established and has roots that can be traced back to the city’s founding. A series of smaller programs linked together, and to the larger urban context will serve as a means of re-energizing this forgotten district, and re-energizing a city that has been in steady decline for the last several decades. The project must draw on several community partners and organizations, and the unique skills, services, and opportunities that can offer each other, and the community as a whole

Separating invariants for arbitrary linear actions of the additive group

We consider an arbitrary representation of the additive group G_a over a field of characteristic zero and give an explicit description of a finite separating set in the corresponding ring of invariants

The Cohen-Macaulay property of separating invariants of finite groups

In the case of finite groups, a separating algebra is a subalgebra of the ring of invariants which separates the orbits. Although separating algebras are often better behaved than the ring of invariants, we show that many of the criteria which imply the ring of invariants is non Cohen-Macaulay actually imply that no graded separating algebra is Cohen-Macaulay. For example, we show that, over a field of positive characteristic p, given sufficiently many copies of a faithful modular representation, no graded sep- arating algebra is Cohen-Macaulay. Furthermore, we show that, for a p-group, the existence of a Cohen-Macaulay graded separat- ing algebra implies the group is generated by bireflections. Ad- ditionally, we give an example which shows that Cohen-Macaulay separating algebras can occur when the ring of invariants is not Cohen-Macaulay

Brownian Dynamics of a Sphere Between Parallel Walls

We describe direct imaging measurements of a colloidal sphere's diffusion between two parallel surfaces. The dynamics of this deceptively simple hydrodynamically coupled system have proved difficult to analyze. Comparison with approximate formulations of a confined sphere's hydrodynamic mobility reveals good agreement with both a leading-order superposition approximation as well as a more general all-images stokeslet analysis.Comment: 4 pages, 3 figures, REVTeX with PostScript figure

Plasticized Starch/ Tunicin Whiskers Nanocomposites : 1. Structural Analysis

International audienceNanocomposite materials were obtained using glycerol plasticized starch as the matrix and a colloidal suspension of cellulose whiskers as the reinforcing phase. The cellulose whiskers, prepared from tunicin, consisted of slender parallelepiped rods with a high aspect ratio. After mixing the raw materials and gelatinization of starch, the resulting suspension was cast and evaporated under vacuum. The composites were conditioned at various moisture contents in order to evaluate the effect of this parameter on the composite structure. Th

Complementarities Between Physical Modelling and Computational Fluid Dynamics for an Ecological Continuity Project

This study presents a comparison between physical modelling and computational fluid dynamics (CFD) for investigating ecological continuity of the Poutès dam modification project. Water depth and velocity measurements have been carried out in the whole physical model. A CFD model has been built based on the geometry of the physical model. Simulations were performed using the OpenFOAM software and the InterFoam solver. Water depths and velocities have been extracted from the numerical model and compared to measurements. The agreement is very good for water depths and quite good for velocities

A shadowing problem in the detection of overlapping communities: lifting the resolution limit through a cascading procedure

Community detection is the process of assigning nodes and links in significant communities (e.g. clusters, function modules) and its development has led to a better understanding of complex networks. When applied to sizable networks, we argue that most detection algorithms correctly identify prominent communities, but fail to do so across multiple scales. As a result, a significant fraction of the network is left uncharted. We show that this problem stems from larger or denser communities overshadowing smaller or sparser ones, and that this effect accounts for most of the undetected communities and unassigned links. We propose a generic cascading approach to community detection that circumvents the problem. Using real and artificial network datasets with three widely used community detection algorithms, we show how a simple cascading procedure allows for the detection of the missing communities. This work highlights a new detection limit of community structure, and we hope that our approach can inspire better community detection algorithms.Comment: 14 pages, 12 figures + supporting information (5 pages, 6 tables, 3 figures

Spread of infectious disease and social awareness as parasitic contagions on clustered networks

There is a rich history of models for the interaction of a biological contagion like influenza with the spread of related information such as an influenza vaccination campaign. Recent work on the spread of interacting contagions on networks has highlighted that these interacting contagions can have counter-intuitive interplay with network structure. Here we generalize one of these frameworks to tackle three important features of the spread of awareness and disease: one, we model the dynamics on highly clustered, cliquish, networks to mimic the role of workplaces and households; two, the awareness contagion affects the spread of the biological contagion by reducing its transmission rate where an aware or vaccinated individual is less likely to be infected; and three, the biological contagion also affects the spread of the awareness contagion but by increasing its transmission rate where an infected individual is more receptive and more likely to share information related to the disease. Under these conditions, we find that increasing network clustering, which is known to hinder disease spread, can actually allow them to sustain larger epidemics of the disease in models with awareness. This counter-intuitive result goes against the conventional wisdom suggesting that random networks are justifiable as they provide worst-case scenario forecasts. To further investigate this result, we provide a closed-form criterion based on a two-step branching process (i.e., the numbers of expected tertiary infections) to identify different regions in parameter space where the net effect of clustering and co-infection varies. Altogether, our results highlight once again the need to go beyond random networks in disease modeling and illustrate the type of analysis that is possible even in complex models of interacting contagions.Comment: 15 pages, 4 figure