Paper Session I-C - The Role of the University in Commercial Launch Activities

Success of the U.S. in the international space arena depends largely on our ability to reduce the cost-to-orbit of payloads. To reduce costs, significant effort must be expended to upgrade commercial launch vehicles, processing facilities and operational procedures. The universities have much to contribute to such an effort. This paper discusses the positive role that universities can play in helping industry and government be more successful in commercial space. Activities include needs assessments* problem definition, research, test and evaluation, business assistance, and education. Emphasis is placed on establishing a permanent capability to continuously advance launch systems technology, and on developing education and research programs that are complementary to each other

ICONA: Inter Cluster ONOS Network Application

Several Network Operating Systems (NOS) have been proposed in the last few years for Software Defined Networks; however, a few of them are currently offering the resiliency, scalability and high availability required for production environments. Open Networking Operating System (ONOS) is an open source NOS, designed to be reliable and to scale up to thousands of managed devices. It supports multiple concurrent instances (a cluster of controllers) with distributed data stores. A tight requirement of ONOS is that all instances must be close enough to have negligible communication delays, which means they are typically installed within a single datacenter or a LAN network. However in certain wide area network scenarios, this constraint may limit the speed of responsiveness of the controller toward network events like failures or congested links, an important requirement from the point of view of a Service Provider. This paper presents ICONA, a tool developed on top of ONOS and designed in order to extend ONOS capability in network scenarios where there are stringent requirements in term of control plane responsiveness. In particular the paper describes the architecture behind ICONA and provides some initial evaluation obtained on a preliminary version of the tool.Comment: Paper submitted to a conferenc

Service level Indication: A proposal for QoS monitoring in SLA -based multidomain networks

The offering of QoS based communication services has to face several challenges. Among these, the provisioning of an open and formalised framework for the collection and interchange of monitoring and performance data is feit as one of the most important issues to be solved. Indeed, this is true in seenarios where multiple providers are teaming (intentionally or not) for the construction of a complex service to be sold to a final user, like in the case of the creation of a virtual private network spanning multiple network Operators and infrastructures. In this case, failures in providing certain required Ievels in the quality parameters should be dealt with an immediate attribution of responsibility across the different entities involved in the end-to-end provisioning of the service. But also in cases apparently much simpler, for example when an user requires a video strearning service across a single operator network infrastructure, there is a demand for mechanisms for the measurement of the received quality of service across all the elements involved in the service provisioning: the server system, the network infrastructure, the dient terminal and the user application. lt is clear that this is a complex problem, involving different technologies, disciplines and research areas. In this paper, starting from the ongoing work in the definition of standard interfaces for the Quality of Service negotiation (Service Level Agreements) and control (Service Level Specifications), as weil as from the work ongoing in the IPFIX and IPPM working groups from the IETF, we introduce a new document specifically for delivering monitoring information to user applications. We called such a document Service Level Indication. We here aim at sketching a possible starting point for a research discussion. © 2003 by Springer Science+Business Media Dordrecht

A decrease of calcitonin serum concentrations less than 50 percent 30 minutes after thyroid surgery suggests incomplete C-cell tumor tissue removal

The prognosis of medullary thyroid carcinoma (MTC) depends on the completeness of the first surgical treatment. To date, it is not possible to predict whether the tumor has been completely removed after surgery. The aim of this study was to evaluate the reliability of an intraoperative calcitonin monitoring as a predictor of the final outcome after surgery in patients with MTC

Palabras de los miembros estudiantes del Comité Editor

Al emprender este proyecto, nos preguntábamos como estudiantes qué significa la elaboración de la tesis de grado de Psicología. Encontramos que etimológicamente, Tesis proviene del griego y significa "lugar", "posición", en el sentido de propuesta, afirmación, proposición. En latín quiere decir "conclusión".publishedVersionFil: Shapoff, Virginia. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Ventre, Agostína M. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina

Ascertaining price formation in cryptocurrency markets with machine learning

The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using machine learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a machine learning approach to predict the direction of the mid-price changes on the upcoming tick. We show that there are universal features amongst cryptocurrencies which lead to models outperforming asset-specific ones. We also show that there is little point in feeding machine learning models with long sequences of data points; predictions do not improve. Furthermore, we solve the technical challenge to design a lean predictor, which performs well on live data downloaded from crypto exchanges. A novel retraining method is defined and adopted towards this end. Finally, the trade-off between model accuracy and frequency of training is analyzed in the context of multi-label prediction. Overall, we demonstrate that promising results are possible for cryptocurrencies on live data, by achieving a consistent 78% accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs. US dollars