Informativeness of sleep cycle features in Bayesian assessment of newborn electroencephalographic maturation

Clinical experts assess the newborn brain development by analyzing and interpreting maturity-related features in sleep EEGs. Typically, these features widely vary during the sleep hours, and their informativeness can be different in different sleep stages. Normally, the level of muscle and electrode artifacts during the active sleep stage is higher than that during the quiet sleep that could reduce the informative-ness of features extracted from the active stage. In this paper, we use the methodology of Bayesian averaging over Decision Trees (DTs) to assess the newborn brain maturity and explore the informativeness of EEG features extracted from different sleep stages. This methodology has been shown providing the most accurate inference and estimates of uncertainty, while the use of DT models enables to find the EEG features most important for the brain maturity assessment

Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity

We explored the feature extraction techniques for Bayesian assessment of EEG maturity of newborns in the context that the continuity of EEG is the most important feature for assessment of the brain development. The continuity is associated with EEG “stationarity” which we propose to evaluate with adaptive segmentation of EEG into pseudo-stationary intervals. The histograms of these intervals are then used as new features for the assessment of EEG maturity. In our experiments, we used Bayesian model averaging over decision trees to differentiate two age groups, each included 110 EEG recordings. The use of the proposed EEG features has shown, on average, a 6% increase in the accuracy of age differentiation

Nahm's Equations and Rational Maps from CP1\mathbb{C}\mathrm{P}^1 to CPn\mathbb{C}\mathrm{P}^n

We consider Nahm's equations on a bounded open interval with first order poles at the ends. By imposing further boundary conditions, extended moduli spaces are identified with spaces of rational maps from $\mathbb{C}\mathrm{P}^1$ to $\mathbb{C}\mathrm{P}^n$. Having the residues at one end define sums of irreducible representations of $\mathfrak{su}(2)$, the dimensions of those summands correspond to holomorphic charge on the rational map side. Technical difficulties arise due to half powers in the boundary conditions. Further, a symplectomorphic identification with moduli spaces corresponding to spaces of rational maps into complete flag varieties is given.Comment: 41 pages, 5 figure

Wissen im Doppelpack : Fallbasierte Expertensystemshell CBR Express

Die fallbasierte Expertensystemshell CBR Express wird vorgestellt

"If You Can't Beat them, Join them": A Usability Approach to Interdependent Privacy in Cloud Apps

Cloud storage services, like Dropbox and Google Drive, have growing ecosystems of 3rd party apps that are designed to work with users' cloud files. Such apps often request full access to users' files, including files shared with collaborators. Hence, whenever a user grants access to a new vendor, she is inflicting a privacy loss on herself and on her collaborators too. Based on analyzing a real dataset of 183 Google Drive users and 131 third party apps, we discover that collaborators inflict a privacy loss which is at least 39% higher than what users themselves cause. We take a step toward minimizing this loss by introducing the concept of History-based decisions. Simply put, users are informed at decision time about the vendors which have been previously granted access to their data. Thus, they can reduce their privacy loss by not installing apps from new vendors whenever possible. Next, we realize this concept by introducing a new privacy indicator, which can be integrated within the cloud apps' authorization interface. Via a web experiment with 141 participants recruited from CrowdFlower, we show that our privacy indicator can significantly increase the user's likelihood of choosing the app that minimizes her privacy loss. Finally, we explore the network effect of History-based decisions via a simulation on top of large collaboration networks. We demonstrate that adopting such a decision-making process is capable of reducing the growth of users' privacy loss by 70% in a Google Drive-based network and by 40% in an author collaboration network. This is despite the fact that we neither assume that users cooperate nor that they exhibit altruistic behavior. To our knowledge, our work is the first to provide quantifiable evidence of the privacy risk that collaborators pose in cloud apps. We are also the first to mitigate this problem via a usable privacy approach.Comment: Authors' extended version of the paper published at CODASPY 201

Neuartige Plattenwärmeübertrager - Teil B: Zum Einfluss von Bypass-Strömung auf Druckverlust und Wärmeübergang in Plattenwärmeübertragern

Im Rahmen von experimentellen Leistungstests an Plattenwärmeübertragern tritt u.a. die Frage nach dem Einfluss von Bypassströmungen im Mantelraum auf. Zweifellos werden sowohl Druckverlust als auch Wärmeübergang davon beeinflusst. In der vorliegenden Arbeit soll dieser Frage nachgegangen werden