Multiplicative versus additive noise in multi-state neural networks

The effects of a variable amount of random dilution of the synaptic couplings in Q-Ising multi-state neural networks with Hebbian learning are examined. A fraction of the couplings is explicitly allowed to be anti-Hebbian. Random dilution represents the dying or pruning of synapses and, hence, a static disruption of the learning process which can be considered as a form of multiplicative noise in the learning rule. Both parallel and sequential updating of the neurons can be treated. Symmetric dilution in the statics of the network is studied using the mean-field theory approach of statistical mechanics. General dilution, including asymmetric pruning of the couplings, is examined using the generating functional (path integral) approach of disordered systems. It is shown that random dilution acts as additive gaussian noise in the Hebbian learning rule with a mean zero and a variance depending on the connectivity of the network and on the symmetry. Furthermore, a scaling factor appears that essentially measures the average amount of anti-Hebbian couplings.Comment: 15 pages, 5 figures, to appear in the proceedings of the Conference on Noise in Complex Systems and Stochastic Dynamics II (SPIE International

Using network science to analyze football passing networks: dynamics, space, time and the multilayer nature of the game

From the diversity of applications of Network Science, in this Opinion Paper we are concerned about its potential to analyze one of the most extended group sports: Football (soccer in U.S. terminology). As we will see, Network Science allows addressing different aspects of the team organization and performance not captured by classical analyses based on the performance of individual players. The reason behind relies on the complex nature of the game, which, paraphrasing the foundational paradigm of complexity sciences "can not be analyzed by looking at its components (i.e., players) individually but, on the contrary, considering the system as a whole" or, in the classical words of after-match interviews "it's not just me, it's the team".Comment: 7 pages, 1 figur

Electronic control/display interface technology

An effort to produce a representative workstation for the Space Station Data Management Test Bed that provides man/machine interface design options for consolidating, automating, and integrating the space station work station, and hardware/software technology demonstrations of space station applications is discussed. The workstation will emphasize the technologies of advanced graphics engines, advanced display/control medias, image management techniques, multifunction controls, and video disk utilizations

Developing advanced methods to predict air traffic network growth

This dissertation describes a forecasting methodology that takes into account changes in the connectivity of an air transportation system and assesses the impact at other levels of the network, such as route demand and air traffic levels. To achieve this, the modelling framework looks at city pair demand generation, route demand assignment and air traffic estimation. While generating air traffic forecasts, the resulting model is also intended to highlight the most important factors driving air traffic network growth. This is achieved by considering a larger set of drivers than those considered in existing methodologies and research as well as exploring the use of alternative modelling techniques. Network evolution is incorporated in the method through an airport connectivity model which identifies how and when airport-pairs across the network change their connectivity status. The problem is split into two models: one identifying those airport-pairs that are added to the network; and another one identifying those airport-pairs that are removed from the network. The modelling approach explores the use of network theory metrics along with other input variables, such as passenger demand, to see whether existing models employing only network theory metrics could be improved. The impact of network evolution is assessed by the effect on air itinerary shares. Two itinerary choice models are developed using two different modelling approaches: multinomial logit and neural networks. While the multinomial logit formulation is the most common approach used to model itinerary shares, only few studies have looked at modelling itinerary shares at the network level. Neural networks have yet to be explored in this field. In this research, air itinerary choice models have been developed at the most aggregate level, using open-source booking data, for a large group of city-pairs within the US Air Transportation System. The output of the itinerary choice models, influenced by the consideration of network evolution, is then used to project air traffic levels and assess the impact of network structure changes in the number of operations in the US ATS. The results reflect the complexity behind network evolution, especially for cases when a mature system is considered (e.g. US ATS): comparisons between the case of a static network and the case when network evolution is considered indicate that the impact of network changes on overall system metrics is relatively minor in the US. However, they indicate that changes in fossil fuel prices may influence changes in the overall network characteristics, and consequently network evolution. The results also prove the feasibility of estimating a single itinerary choice model at the network level for an entire air transportation system. Although the multinomial logit model results have better accuracy, the potential of neural networks for this purpose is also demonstrated, the latter being more representative of the hub-and-spoke network strategy

Application of Data Mining to forecast Air Traffic: A 3-Stage Model using Discrete Choice Modeling

The main goal of this study centers on developing an aggregate air itinerary share model estimated at the city-pair level within the US air transportation system. This route demand assignment model is part of a new modeling approach that has as its ultimate output the prediction of detailed traffic information for the US air transportation system. In this approach, city-pair demand generation, route demand assignment and air traffic levels estimations are completed in 3 different stages within a single framework. Aiming to fully develop the overall model, in this paper we focus on estimating the 2nd stage, the air itinerary choice model. In order to achieve this, the first approach taken applies a multinomial logit model and uses a combination of stated preferences (SP) and revealed preferences (RP) data to estimate the model. By using a mixed dataset, we attempt to improve the RP model results, which often perform poorly due to high demand inelasticity. Preliminary results show the potential of this approach, although further analysis is required to understand the results obtained. For the final paper, different approaches and further interactions among the model attributes will be applied to improve the model’s performance

Air itinerary shares estimation using multinomial logit models

The main goal of this study is the development of an aggregate air itinerary market share model. In order to achieve this, multinomial logit models are applied to distribute the city-pair passenger demand across the available itineraries. The models are developed at an aggregate level using open-source booking data for a large group of city-pairs within the US air transport system. Although there is a growing trend in the use of discrete choice models in the aviation industry, existing air itinerary share models are mostly focused on supporting carrier decision-making. Consequently, those studies define itineraries at a more disaggregate level using variables describing airlines and time preferences. In this study, we define itineraries at a more aggregate level, i.e. as a combination of flight segments between an origin and destination, without further insight into service preferences. Although results show some potential for this approach, there are challenges associated with prediction performance and computational intensity

Validation of the CAchexia SCOre (CASCO). Staging cancer patients: The use of miniCASCO as a simplified tool

The CAchexia SCOre (CASCO) was described as a tool for the staging of cachectic cancer patients. The aim of this study is to show the metric properties of CASCO in order to classify cachectic cancer patients into three different groups, which are associated with a numerical scoring. The final aim was to clinically validate CASCO for its use in the classification of cachectic cancer patients in clinical practice. We carried out a case -control study that enrolled prospectively 186 cancer patients and 95 age-matched controls. The score includes five components: (1) body weight loss and composition, (2) inflammation/metabolic disturbances/immunosuppression, (3) physical performance, (4) anorexia, and (5) quality of life. The present study provides clinical validation for the use of the score. In order to show the metric properties of CASCO, three different groups of cachectic cancer patients were established according to the results obtained with the statistical approach used: mild cachexia (15 â\u89¤ Ã\u97 â\u89¤ 28), moderate cachexia (29 â\u89¤ Ã\u97 â\u89¤ 46), and severe cachexia (47 â\u89¤ Ã\u97 â\u89¤ 100). In addition, a simplified version of CASCO, MiniCASCO (MCASCO), was also presented and it contributes as a valid and easy-to-use tool for cachexia staging. Significant statistically correlations were found between CASCO and other validated indexes such as Eastern Cooperative Oncology Group (ECOG) and the subjective diagnosis of cachexia by specialized oncologists. A very significant estimated correlation between CASCO and MCASCO was found that suggests that MCASCO might constitute an easy and valid tool for the staging of the cachectic cancer patients. CASCO and MCASCO provide a new tool for the quantitative staging of cachectic cancer patients with a clear advantage over previous classifications