The Contamination of Practices: How Practice Theories Matter in Multiple Domains

This introduction presents the genesis of The Contamination of Practices: How Practice Theories Matter in Multiple Domains Symposium. The Symposium is the outcome of a research network established to explore new ways to adopt, criticize and expand the approaches that fall under the definition of practice theory, in relation to different topics, and to answer multiple research questions. The shared foundation underlying the contributions is developed around the metaphor of \u201ccontamination\u201d: while up to the present the efforts of scholars of major practice theory have been to sharpened to specify a common theoretical framework, we argue that practice theory is at the verge of entering a new stage of diffusion and contamination. Contamination is intended here in a double sense: on the one hand, practice theory is contaminating several fields and domains of social research; on the other hand, its conceptual framework is also being contaminated by other intellectual and theoretical traditions, as it is being innovatively adopted and adapted based on the different topics and questions it addresses. Each of the nine articles comprising the Symposium addresses this contamination in a specific way

Introduction to special issue on Eye and Zoonosis - from the guest editors

Papers of this special issue are based on the presentations given in the Congress “Eye and Zoonosis” - October 10-11th 2008, Parma (Italy). This issue aims to provide researchers with timely update on a number of important topics on Zoonosis in Ophthalmology

Optimal sales-mix and generation plan in a two-stage electricity market

A bi-level stochastic programming problem is used to model the optimal decision of a risk averse electricity producer, interacting in a two-stage market with cost minimizer competitors. His decision variables include the distribution of production (which plant of different technologies and variable costs to operate) and the sales-mix (how much generation to commit to bilateral contracts and spot market). To enhance computation times, the bi-level problem is transformed into a Mixed-Integer Linear Problem (MILP) by applying sophisticated linearization techniques. Electricity demand, Renewable Energy Sources (RES) generation and production costs are different sources of uncertainty. A copula method is used to generate scenarios under different correlations values (between RES generation and demand), to analyze the impact of correlation on the optimal solution. The model is tested through extensive numerical simulations based on data from the Spanish electricity market. The results show that correlation and risk aversion have a relevant impact on how sales-mix and generation plan decisions should combine optimally

In this issue

reserved3simixedMora, E.; Rocamora, A.; Volonte', P.Mora, E.; Rocamora, A.; Volonte', PAOLO GAETAN

Running Faster Together: Huge Speed up of Thermal Ratchets due to Hydrodynamic Coupling

We present simulations that reveal a surprisingly large effect of hydrodynamic coupling on the speed of thermal ratchet motors. The model that we use considers particles performing thermal ratchet motion in a hydrodynamic solvent. Using particle-based, mesoscopic simulations that maintain local momentum conservation, we analyze quantitatively how the coupling to the surrounding fluid affects ratchet motion. We find that coupling can increase the mean velocity of the moving particles by almost 2 orders of magnitude, precisely because ratchet motion has both a diffusive and a deterministic component. The resulting coupling also leads to the formation of aggregates at longer times. The correlated motion that we describe increases the efficiency of motor-delivered cargo transport and we speculate that the mechanism that we have uncovered may play a key role in speeding up molecular motor-driven intracellular transport

Bistability, Oscillations, and Bidirectional Motion of Ensemble of Hydrodynamically Coupled Molecular Motors

We analyze the collective behavior of hydrodynamically coupled molecular motors. We show that the local fluxes induced by motor displacement can induce the experimentally observed bidirectional motion of cargoes and vesicles. By means of a mean-field approach we show that sustained oscillations as well as bistable collective motor motion arise even for very large collection of motors, when thermal noise is irrelevant. The analysis clarifies the physical mechanisms responsible for such dynamics by identifying the relevant coupling parameter and its dependence on the geometry of the hydrodynamic coupling as well as on system size. We quantify the phase diagram for the different phases that characterize the collective motion of hydrodynamically coupled motors and show that sustained oscillations can be reached for biologically relevant parameters, hence, demonstrating the relevance of hydrodynamic interactions in intracellular transport

Knowledge Distillation for Federated Learning: a Practical Guide

Federated Learning (FL) enables the training of Deep Learning models without centrally collecting possibly sensitive raw data. This paves the way for stronger privacy guarantees when building predictive models. The most used algorithms for FL are parameter-averaging based schemes (e.g., Federated Averaging) that, however, have well known limits: (i) Clients must implement the same model architecture; (ii) Transmitting model weights and model updates implies high communication cost, which scales up with the number of model parameters; (iii) In presence of non-IID data distributions, parameter-averaging aggregation schemes perform poorly due to client model drifts. Federated adaptations of regular Knowledge Distillation (KD) can solve and/or mitigate the weaknesses of parameter-averaging FL algorithms while possibly introducing other trade-offs. In this article, we provide a review of KD-based algorithms tailored for specific FL issues.Comment: 9 pages, 1 figur