407 resultados para resting-state networks
Resumo:
Mixed use typologies and pedestrian networks are two strategies commonly applied in design of the contemporary city. These approaches, aimed towards the creation of a more sustainalble urban environment, have their roots in the traditional, pre-industrial towns; they characterize urban form, articulating the tension between privaate and public realms through a series of typological variations as well as stimulating commercial activity in the city centre. Arcades, loggias and verandas are just some of the elements which can mediate this tension. Historically they have defined physical and social spaces with particular character; in the contemporary city these features are applied to deform the urban form and create a porous, dynamic morphology. This paper, comparing case studies from Italy, Japan and Australia, investigates how the design of the transition zone can define hybrid pedestrian networks, where a clear distinction between the public and private realms is no longer applicable. Pedestrians use the city in a dynamic way, combining trajectories on the public street with ones on the fringe or inside of the private built environment. In some cases, cities offer different pedestrian network possibilities at different times, as the commercial precints are subject to variations in accessibility across various timeframes. These walkable systems have an impact on the urban form and identity of places, redefining typologies and requiring an in depth analysis through plan, section and elevation diagrams.
Resumo:
Ethernet is a key component of the standards used for digital process buses in transmission substations, namely IEC 61850 and IEEE Std 1588-2008 (PTPv2). These standards use multicast Ethernet frames that can be processed by more than one device. This presents some significant engineering challenges when implementing a sampled value process bus due to the large amount of network traffic. A system of network traffic segregation using a combination of Virtual LAN (VLAN) and multicast address filtering using managed Ethernet switches is presented. This includes VLAN prioritisation of traffic classes such as the IEC 61850 protocols GOOSE, MMS and sampled values (SV), and other protocols like PTPv2. Multicast address filtering is used to limit SV/GOOSE traffic to defined subsets of subscribers. A method to map substation plant reference designations to multicast address ranges is proposed that enables engineers to determine the type of traffic and location of the source by inspecting the destination address. This method and the proposed filtering strategy simplifies future changes to the prioritisation of network traffic, and is applicable to both process bus and station bus applications.
Resumo:
Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.
Resumo:
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.
Resumo:
Raman spectroscopy has been used to study vanadates in the solid state. The molecular structure of the vanadate minerals vésigniéite [BaCu3(VO4)2(OH)2] and volborthite [Cu3V2O7(OH)2·2H2O] have been studied by Raman spectroscopy and infrared spectroscopy. The spectra are related to the structure of the two minerals. The Raman spectrum of vésigniéite is characterized by two intense bands at 821 and 856 cm−1 assigned to ν1 (VO4)3− symmetric stretching modes. A series of infrared bands at 755, 787 and 899 cm−1 are assigned to the ν3 (VO4)3− antisymmetric stretching vibrational mode. Raman bands at 307 and 332 cm−1 and at 466 and 511 cm−1 are assigned to the ν2 and ν4 (VO4)3− bending modes. The Raman spectrum of volborthite is characterized by the strong band at 888 cm−1, assigned to the ν1 (VO3) symmetric stretching vibrations. Raman bands at 858 and 749 cm−1 are assigned to the ν3 (VO3) antisymmetric stretching vibrations; those at 814 cm−1 to the ν3 (VOV) antisymmetric vibrations; that at 508 cm−1 to the ν1 (VOV) symmetric stretching vibration and those at 442 and 476 cm−1 and 347 and 308 cm−1 to the ν4 (VO3) and ν2 (VO3) bending vibrations, respectively. The spectra of vésigniéite and volborthite are similar, especially in the region of skeletal vibrations, even though their crystal structures differ.
Resumo:
Bistability arises within a wide range of biological systems from the λ phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. In this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.
Resumo:
This paper presents the method and results of a survey of 27 of the 33 Australian universities teaching engineering education in late 2007, undertaken by The Natural Edge Project (hosted by Griffith University and the Australian National University) and supported by the National Framework for Energy Efficiency. This survey aimed to ascertain the extent of energy efficiency (EE) education, and to identify preferred methods to assist in increasing the extent to which EE education is embedded in engineering curriculum. In this paper the context for the survey is supported by a summary of the key results from a variety of surveys undertaken over the last decade internationally. The paper concludes that EE education across universities and engineering disciplines in Australia is currently highly variable and ad hoc. Based on the results of the survey, this paper highlights a number of preferred options to support educators to embed sustainability within engineering programs, and future opportunities for monitoring EE, within the context of engineering education for sustainable development (EESD).
Resumo:
Objective: To highlight the registration issues for nurses who wish to practice nationally, particularly those practicing within the telehealth sector. Design: As part of a national clinical research study, applications were made to every state and territory for mutual recognition of nursing registration and fee waiver for telenursing cross boarder practice for a period of three years. These processes are described using a case study approach. Outcome: The aim of this case study was to achieve registration in every state and territory of Australia without paying multiple fees by using mutual recognition provisions and the cross-border fee waiver policy of the nurse regulatory authorities in order to practice telenursing. Results: Mutual recognition and fee waiver for cross-border practice was granted unconditionally in two states: Victoria (Vic) and Tasmania (Tas), and one territory: the Northern Territory (NT). The remainder of the Australian states and territories would only grant temporary registration for the period of the project or not at all, due to policy restrictions or nurse regulatory authority (NRA) Board decisions. As a consequence of gaining fee waiver the annual cost of registration was a maximum of $145 per annum as opposed to the potential $959 for initial registration and $625 for annual renewal. Conclusions: Having eight individual nurses Acts and NRAs for a population of 265,000 nurses would clearly indicate a case for over regulation in this country. The structure of regulation of nursing in Australia is a barrier to the changing and evolving role of nurses in the 21st century and a significant factor when considering workforce planning.