6,350 research outputs found
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
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Theoretical loss and gambling intensity: a simulation study
Many recent studies of internet gamblingâparticularly those that have analysed behavioural tracking dataâhave used variables such as âbet sizeâ and ânumber of games playedâ as proxy measures for âgambling intensity.â In this paper, it is argued that the best and most stable measure for Gambling Intensity is the âTheoretical Lossâ (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 gamblersâ 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 âbet sizeâ and ânumber of gamesâ 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
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
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 -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
Advanced MR techniques in glioblastoma imagingâupcoming 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
Bandit Models of Human Behavior: Reward Processing in Mental Disorders
Drawing an inspiration from behavioral studies of human decision making, we
propose here a general parametric framework for multi-armed bandit problem,
which extends the standard Thompson Sampling approach to incorporate reward
processing biases associated with several neurological and psychiatric
conditions, including Parkinson's and Alzheimer's diseases,
attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
We demonstrate empirically that the proposed parametric approach can often
outperform the baseline Thompson Sampling on a variety of datasets. Moreover,
from the behavioral modeling perspective, our parametric framework can be
viewed as a first step towards a unifying computational model capturing reward
processing abnormalities across multiple mental conditions.Comment: Conference on Artificial General Intelligence, AGI-1
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