Dispatcher3 D4.2 - Prototype package (first release) - User manual

This deliverable along with deliverable D4.1. Technical documentation first release consists of the release of the first prototype of Dispatcher3. The release consists of the binaries and Docker version of the prototype (sent to the Topic Manager). The first release prototype package consists of a set on individual machine learning models which can be executed using Jupyter notebooks. It also includes the integration of the outcome of some of these individual models into a visualisation which would be part of the advice generator to provide high-level information to the end users. All models described in the Deliverable D4.1 will be available and executable in this release. Data required to run the models (with some examples) are also provided. If data are public raw sample values are provided, otherwise pre-computed features are delivered so that the models can be run on individual flight examples. The prototypes can be run using local data (provided in the release) or with data stored in cloud storage (Amazon Web Services (AWS)). This deliverable serves as a manual for the execution of the first release prototype software

Dispatcher3 – Machine learning to support flight planning processes

This poster will present the final results of the Clean Sky 2 project Dispatcher3. Dispatcher3 focuses on the use of machine learning techniques to support flight operations prior departure with holding predictions, runway at arrival estimation and fuel deviations pre-departure to support the flight crew, and ATFM and reactionary delays on D-1 to support the duty manage

Dispatcher3 – Machine learning for efficient flight planning - Approach and challenges for data-driven prototypes in air transport

Machine learning techniques to support decision making processes are in trend. These are particularly relevant in the context of flight management where large datasets of planned and realised operations are available. Current operations experience discrepancies between planned and executed flight plan, these might be due to external factors (e.g. weather, congestion) and might lead to sub-optimal decisions (e.g. recovering delay (burning extra fuel) when no holding is expected at arrival and therefore it was no needed). Dispatcher3 produces a set of machine learning models to support flight crew pre-departure, with estimations on expected holding at arrival, runway in use and fuel usage, and the airline’s duty manager on pre-tactical actions, with models trained with a larger look ahead time for ATFM and reactionary delay estimations. This paper describes the prototype architecture and approach of Dispatcher3 with particular focus on the challenges faced by this type of data-driven machine learning models in the field of air transport ranging: from technical aspects such as data leakage to operational requirements such as the consideration and estimation of uncertainty. These considerations should be relevant for projects which try to use machine learning in the field of aviation in general

The Running of the Cosmological and the Newton Constant controlled by the Cosmological Event Horizon

We study the renormalisation group running of the cosmological and the Newton constant, where the renormalisation scale is given by the inverse of the radius of the cosmological event horizon. In this framework, we discuss the future evolution of the universe, where we find stable de Sitter solutions, but also "big crunch"-like and "big rip"-like events, depending on the choice of the parameters in the model.Comment: 14 pages, 7 figures, minor improvements, references adde

Eisenstein Series in String Theory

We discuss the relevance of Eisenstein series for representing certain G(Z)-invariant string theory amplitudes which receive corrections from BPS states only. The Eisenstein series are constructed using G(Z)-invariant mass formulae and are manifestly invariant modular functions on the symmetric space K\G(R) of non-compact type, with K the maximal compact subgroup of G(R). In particular, we show how Eisenstein series of the T-duality group SO(d,d,Z) can be used to represent one- and g-loop amplitudes in compactified string theory. We also obtain their non-perturbative extensions in terms of the Eisenstein series of the U-duality group E_{d+1(d+1)}(Z).Comment: 11 pages, Latex, submitted to Proceedings of Strings '99, published versio

Pasta consumption and connected dietary habits: Associations with glucose control, adiposity measures, and cardiovascular risk factors in people with type 2 diabetes—TOSCA.IT study

Background: Pasta is a refined carbohydrate with a low glycemic index. Whether pasta shares the metabolic advantages of other low glycemic index foods has not really been investigated. The aim of this study is to document, in people with type-2 diabetes, the consumption of pasta, the connected dietary habits, and the association with glucose control, measures of adiposity, and major cardiovascular risk factors. Methods: We studied 2562 participants. The dietary habits were assessed with the European Prospective Investigation into Cancer and Nutrition (EPIC) questionnaire. Sex-specific quartiles of pasta consumption were created in order to explore the study aims. Results: A higher pasta consumption was associated with a lower intake of proteins, total and saturated fat, cholesterol, added sugar, and fiber. Glucose control, body mass index, prevalence of obesity, and visceral obesity were not significantly different across the quartiles of pasta intake. No relation was found with LDL cholesterol and triglycerides, but there was an inverse relation with HDL-cholesterol. Systolic blood pressure increased with pasta consumption; but this relation was not confirmed after correction for confounders. Conclusions: In people with type-2 diabetes, the consumption of pasta, within the limits recommended for total carbohydrates intake, is not associated with worsening of glucose control, measures of adiposity, and major cardiovascular risk factors

Router-level community structure of the Internet Autonomous Systems

The Internet is composed of routing devices connected between them and organized into independent administrative entities: the Autonomous Systems. The existence of different types of Autonomous Systems (like large connectivity providers, Internet Service Providers or universities) together with geographical and economical constraints, turns the Internet into a complex modular and hierarchical network. This organization is reflected in many properties of the Internet topology, like its high degree of clustering and its robustness. In this work, we study the modular structure of the Internet router-level graph in order to assess to what extent the Autonomous Systems satisfy some of the known notions of community structure. We show that the modular structure of the Internet is much richer than what can be captured by the current community detection methods, which are severely affected by resolution limits and by the heterogeneity of the Autonomous Systems. Here we overcome this issue by using a multiresolution detection algorithm combined with a small sample of nodes. We also discuss recent work on community structure in the light of our results

Observation of inhibited electron-ion coupling in strongly heated graphite

Creating non-equilibrium states of matter with highly unequal electron and lattice temperatures (Tele≠Tion) allows unsurpassed insight into the dynamic coupling between electrons and ions through time-resolved energy relaxation measurements. Recent studies on low-temperature laser-heated graphite suggest a complex energy exchange when compared to other materials. To avoid problems related to surface preparation, crystal quality and poor understanding of the energy deposition and transport mechanisms, we apply a different energy deposition mechanism, via laser-accelerated protons, to isochorically and non-radiatively heat macroscopic graphite samples up to temperatures close to the melting threshold. Using time-resolved x ray diffraction, we show clear evidence of a very small electron-ion energy transfer, yielding approximately three times longer relaxation times than previously reported. This is indicative of the existence of an energy transfer bottleneck in non-equilibrium warm dense matter