113,862 research outputs found
Splitting and Doubling: Spaces for Contemporary Living in Works by Gordon Matta-Clark, Kurt Schwitters and Gregor Schneider
No full textThe thesis addresses the question of dwelling as a challenge and concern in the twenty-first century. It does so on the basis of three works of art, all exercising radical spatial reconfigurations of existing residential buildings. The thesis argues that these works created in the twentieth century bring strategies forward for a contemporary living space of interest today. Furthermore, that the agency of the artistic gesture exceeds the scope of the architectural work when addressing the subject of home and house in critical ways. The importance of this engagement lies in an incompatibility observed between ideas about dwelling and the experience of the contemporary age. A prevalent desire for a permanently settled and stable living space is at odds with increasingly transient and nomadic present-day lifestyles – the thesis asks how come such concepts without application endure.
Literary works, concerned with the process of modernisation in the twentieth century, are called upon to qualify this problem of dwelling in our time. While the texts provide insight into the dialectics of the modern, the chosen works of art unfold three living spaces settled in the moment of their making. When answering the immediate contextual setting with an environment for living beyond conventional building practices, Gordon Matta-Clark’s Splitting (1974), Kurt Schwitters’ Merzbau (1927-37) and Gregor Schneider’s HAUS u r (1985-today) give clues to the nature of the contemporary dwelling. As a living space beyond conceptualisation, this dwelling does not require a whole house to be held in place nor does it rely on walls for spatial differentiation. Instead, a framework for coexistence is articulated as a space of resistance to the forces of the modern, threatening to render all dwellers homeless. The thesis challenges the contemporary architect with the task of participating in the creation of this space
Managing information constraints over networks through the lens of configuration functions
No full textThis work deals with networks of agents that exchange information under communication constraints. As a first contribution, the theory of configuration functions is exploited to obtain a general abstract formulation of the network information as a function of the network constraints. As a second contribution, two classic network paradigms are examined: i) a decentralized architecture with remote fusion center; and ii) a fully-flat decentralized architecture with local data exchange between neighboring agents. It is shown how these paradigms match well with the general formulation in terms of configuration functions. Finally, the statistical concentration properties of configuration functions are exploited to characterize the information growth rate under both the aforementioned network paradigms, revealing the thermodynamic deterministic behavior that emerges with high probability as the network size scales to infinity
An Internet of Things Architecture for Lab-scale Prototypes of Real-Time Simulation
No full textRecently, new technologies for data acquisition, storing and communication enabled to improve the performances of manufacturing systems with new production management and control policies. In the next years, we may expect several architectures exploiting data exchange between a real and a digital manufacturing system. This raises the issue on how to test the frameworks since the availability of real manufacturing systems to researchers is scarce or simply costly. In this work, we propose a novel architecture which is suitable for lab-scale models of manufacturing systems. The developed architecture has been successfully applied to a test case which will be used by an Italian SME as demonstrator for ERP software capabilities
Social Learning with Partial Information Sharing
No full textThis work studies the learning abilities of agents sharing partial beliefs over social networks. The agents observe data that could have risen from one of several hypotheses and interact locally to decide whether the observations they are receiving have risen from a particular hypothesis of interest. To do so, we establish the conditions under which it is sufficient to share partial information about the agents' belief in relation to the hypothesis of interest. Some interesting convergence regimes arise
Partial Information Sharing Over Social Learning Networks
No full textThis work addresses the problem of sharing partial information within social learning strategies. In social learning, agents solve a distributed multiple hypothesis testing problem by performing two operations at each instant: first, agents incorporate information from private observations to form their beliefs over a set of hypotheses; second, agents combine the entirety of their beliefs locally among neighbors. Within a sufficiently informative environment and as long as the connectivity of the network allows information to diffuse across agents, these algorithms enable agents to learn the true hypothesis. Instead of sharing the entirety of their beliefs, this work considers the case in which agents will only share their beliefs regarding one hypothesis of interest, with the purpose of evaluating its validity, and draws conditions under which this policy does not affect truth learning. We propose two approaches for sharing partial information, depending on whether agents behave in a self-aware manner or not. The results show how different learning regimes arise, depending on the approach employed and on the inherent characteristics of the inference problem. Furthermore, the analysis interestingly points to the possibility of deceiving the network, as long as the evaluated hypothesis of interest is close enough to the truth
Adaptation in online social learning
No full textThis work studies social learning under non-stationary conditions. Although designed for online inference, traditional social learning algorithms perform poorly under drifting conditions. To mitigate this drawback, we propose the Adaptive Social Learning (ASL) strategy. This strategy leverages an adaptive Bayesian update, where the adaptation degree can be modulated by tuning a suitable step-size parameter. The learning performance of the ASL algorithm is examined by means of a steady-state analysis. It is shown that, under the regime of small step-sizes: i) consistent learning is possible; ii) and an accurate prediction of the performance can be furnished in terms of a Gaussian approximation
Lab-scale Models of Manufacturing Systems for Testing Real-time Simulation and Production Control Technologies
No full textIn the last years, the increase of data availability together with enhanced computation capabilities empowered researchers to conceive production planning and control methods with real-time inputs. Literature is rich with techniques for using simulation to take production planning and control decisions online. However, it is generally impractical to test these approaches on real systems, and experiments on digital instances are limited because they do not capture the physical aspects. This work proposes to test Real-time Simulation approaches using lab-scale models of manufacturing systems and a software architecture aligned with industrial standards. Such models allow to reproduce material flows and the production control logic of real factory environments. By exploiting this setting to test new approaches and tools, it is possible to increase their own achievable Technology Readiness Level (TRL). The laboratory has been used to set a real-time rescheduling problem on a Flexible Manufacturing System (FMS) model. The test involves simulation models aligned with the current system state for the online identification and implementation of a production scheduling rule that decreases the expected makespan. The results testify that the proposed lab-scale models can be used successfully to test production planning and control approaches
Adaptive Social Learning
No full textThis work proposes a novel strategy for social learning by introducing the critical feature of adaptation. In social learning, several distributed agents update continually their belief about a phenomenon of interest through: i ) direct observation of streaming data that they gather locally; and ii ) diffusion of their beliefs through local cooperation with their neighbors. Traditional social learning implementations are known to learn well the underlying hypothesis (which means that the belief of every individual agent peaks at the true hypothesis), achieving steady improvement in the learning accuracy under stationary conditions. However, these algorithms do not perform well under nonstationary conditions commonly encountered in online learning, exhibiting a significant inertia to track drifts in the streaming data. In order to address this gap, we propose an Adaptive Social Learning (ASL) strategy, which relies on a small step-size parameter to tune the adaptation degree. First, we provide a detailed characterization of the learning performance by means of a steady-state analysis. Focusing on the small step-size regime, we establish that the ASL strategy achieves consistent learning under standard global identifiability assumptions. We derive reliable Gaussian approximations for the probability of error (i.e., of choosing a wrong hypothesis) at each individual agent. We carry out a large deviations analysis revealing the universal behavior of adaptive social learning: the error probabilities decrease exponentially fast with the inverse of the step-size, and we characterize the resulting exponential learning rate. Second, we characterize the adaptation performance by means of a detailed transient analysis, which allows us to obtain useful analytical formulas relating the adaptation time to the step-size. The revealed dependence of the adaptation time and the error probabilities on the step-size highlights the fundamental trade-off between adaptation and learning emerging in adaptive social learning
Survival of probiotic strains in a refrigerated non fermented blended juice using a static in vitro digestion model.
No full textRef. 378/139. ICDF, 2 a 4 de abril 2019. Na publicação: Gomes, F.; Ribeiro, A.; Ribeiro, L.; Matta, V.; Santos, K.; Walter, E
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