Current status of models of Jupiter's magnetosphere in the light of Pioneer data

The salient features of the various models of Jupiter's magnetosphere are compared with each other and with the major findings of Pioneer 10 and 11. No single model explains all the major phenomena detected by the Pioneers. A unified model of Jupiter's magnetosphere is proposed

Theoretical loss and gambling intensity: a simulation study

Many recent studies of internet gambling—particularly those that have analysed behavioural tracking data—have used variables such as ‘bet size’ and ‘number of games played’ as proxy measures for ‘gambling intensity.’ In this paper, it is argued that the best and most stable measure for Gambling Intensity is the ‘Theoretical Loss’ (a product of total bet size and house advantage). In the long run, Theoretical Loss corresponds with the Gross Gaming Revenue generated by commercial gaming operators. For shorter periods of time, Theoretical Loss is the most stable measure of gambling intensity as it is not distorted by gamblers’ occasional wins. Even for single bets, the Theoretical Loss reflects the amount a player is willing to risk. Using a simulation study, with up to 300,000 players playing as many as 13 different games, this paper demonstrates that the bet size and the number of games do not explain the theoretical loss entirely. In fact, there is a large proportion of variance which remains unexplained by measures of ‘bet size’ and ‘number of games’ played. Bet size and the number of games played do not equate to or explain theoretical loss, as neither of these two measures takes into account the house advantage

Advanced MR techniques in glioblastoma imaging—upcoming challenges and how to face them

The management of gliomas has changed dramatically since the presentation of the revised WHO Classification of Tumors of the Central Nervous System in 2016 emphasizing the tumor heterogeneity based on their molecular profile. The need for a more noninvasive characterization of glioblastomas (GBM) by establishing reliable imaging biomarkers to predict patient outcome and improve therapy monitoring is bigger than ever. Multiparametric MRI, including promising newer techniques like electrical property tomography and mapping, may have the potential to provide enough information for intelligent imaging postprocessing algorithms to face the challenge by decoding GBM heterogeneity noninvasively

Revisiting the Core Ontology and Problem in Requirements Engineering

In their seminal paper in the ACM Transactions on Software Engineering and Methodology, Zave and Jackson established a core ontology for Requirements Engineering (RE) and used it to formulate the "requirements problem", thereby defining what it means to successfully complete RE. Given that stakeholders of the system-to-be communicate the information needed to perform RE, we show that Zave and Jackson's ontology is incomplete. It does not cover all types of basic concerns that the stakeholders communicate. These include beliefs, desires, intentions, and attitudes. In response, we propose a core ontology that covers these concerns and is grounded in sound conceptual foundations resting on a foundational ontology. The new core ontology for RE leads to a new formulation of the requirements problem that extends Zave and Jackson's formulation. We thereby establish new standards for what minimum information should be represented in RE languages and new criteria for determining whether RE has been successfully completed.Comment: Appears in the proceedings of the 16th IEEE International Requirements Engineering Conference, 2008 (RE'08). Best paper awar

W49A North - Global or Local or No Collapse?

We attempt to fit observations with 5" resolution of the J=2-1 transition of CS in the directions of H II regions A, B, and G of W49A North as well as observations with 20" resolution of the J=2-1, 3-2, 5-4, and 7-6 transitions in the directions of H II regions A and G by using radiative transfer calculations. These calculations predict the intensity profiles resulting from several spherical clouds along the line of sight. We consider three models: global collapse of a very large (5 pc radius) cloud, localized collapse from smaller (1 pc) clouds around individual H II regions, and multiple, static clouds. For all three models we can find combinations of parameters that reproduce the CS profiles reasonably well provided that the component clouds have a core-envelope structure with a temperature gradient. Cores with high temperature and high molecular hydrogen density are needed to match the higher transitions (e.g. J=7-6) observed towards A and G. The lower temperature, low density gas needed to create the inverse P-Cygni profile seen in the CS J=2-1 line (with 5" beam) towards H II region G arises from different components in the 3 models. The infalling envelope of cloud G plus cloud B creates the absorption in global collapse, cloud B is responsible in local collapse, and a separate cloud, G', is needed in the case of many static clouds. The exact nature of the velocity field in the envelopes for the case of local collapse is not important as long as it is in the range of 1 to 5 km/s for a turbulent velocity of about 6 km/s. High resolution observations of the J=1-0 and 5-4 transitions of CS and C34S may distinguish between these three models. Modeling existing observations of HCO+ and C18O does not allow one to distinguish between the three models but does indicate the existence of a bipolar outflow.Comment: 42 pages, 27 figures, accepted for publication in the ApJS August 2004, v153 issu

Concurrent bandits and cognitive radio networks

We consider the problem of multiple users targeting the arms of a single multi-armed stochastic bandit. The motivation for this problem comes from cognitive radio networks, where selfish users need to coexist without any side communication between them, implicit cooperation or common control. Even the number of users may be unknown and can vary as users join or leave the network. We propose an algorithm that combines an $\epsilon$-greedy learning rule with a collision avoidance mechanism. We analyze its regret with respect to the system-wide optimum and show that sub-linear regret can be obtained in this setting. Experiments show dramatic improvement compared to other algorithms for this setting