Habermas on democracy and human rights

Habermas"s approach to democracy and human rights is a procedural one. In this interview, the connections between deliberative democracy, human rights and the international order are brought forward, as well as the specific traits of a procedural approach to legal, moral and political concerns. Here, the differences between different types of discourses are brought forward as well, since democratic procedures rely upon a majority-principle which cannot be applied to purely moral questions. The interview with Habermas was carried out during his stay in Bergen, Norway 09.11.2005, in connection to the Holberg Prize Award. Interviewer is Simen Øyen, editor of the journal Replikk, University of Bergen, Norway

Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning

Bayesian network structure learning algorithms with limited data are being used in domains such as systems biology and neuroscience to gain insight into the underlying processes that produce observed data. Learning reliable networks from limited data is difficult, therefore transfer learning can improve the robustness of learned networks by leveraging data from related tasks. Existing transfer learning algorithms for Bayesian network structure learning give a single maximum a posteriori estimate of network models. Yet, many other models may be equally likely, and so a more informative result is provided by Bayesian structure discovery. Bayesian structure discovery algorithms estimate posterior probabilities of structural features, such as edges. We present transfer learning for Bayesian structure discovery which allows us to explore the shared and unique structural features among related tasks. Efficient computation requires that our transfer learning objective factors into local calculations, which we prove is given by a broad class of transfer biases. Theoretically, we show the efficiency of our approach. Empirically, we show that compared to single task learning, transfer learning is better able to positively identify true edges. We apply the method to whole-brain neuroimaging data.Comment: 10 page

Sugar palm (Argena pinnata). Potential of sugar palm for bio-ethanol production

The energetic and economic feasibility of bioethanol production from sugar palm is virtually unknown. A positive factor are the potentially very high yields while the long non-productive juvenile phase and the high labor needs can be seen as problematic. Expansion to large scale sugar palm cultivation comes with risks. Small-scale cultivation of sugar palm perfectly fits into local farming systems. In order to make a proper assessment of the value palm sugar as bio-ethanol crop more information is needed

Peripheral Constraint Versus On-line Programming in Rapid Aimed Sequestial Movements

The purpose of this investigation was to examine how the programming and control of a rapid aiming sequence shifts with increased complexity. One objective was to determine if a preprogramming/peripheral constraint explanation is adequate to characterize control of an increasingly complex rapid aiming sequence, and if not, at what point on-line programming better accounts for the data. A second objective was to examine when on-line programming occurs. Three experiments were conducted in which complexity was manipulated by increasing the number of targets from 1 to 11. Initiation- and execution-timing patterns, probe reaction time, and movement kinematics were measured. Results supported the peripheral constraint/pre-programming explanation for sequences up to 7 targets if they were executed in a blocked fashion. For sequences executed in a random fashion (one length followed by a different length), preprogramming did not increase with complexity, and on-line programming occurred without time cost. Across all sequences there was evidence that the later targets created a peripheral constraint on movements to previous targets. We suggest that programming is influenced by two factors: the overall spatial trajectory, which is consistent with Sidaway’s subtended angle hypothesis (1991), and average velocity, with the latter established based on the number of targets in the sequence. As the number of targets increases, average velocity decreases, which controls variability of error in the extent of each movement segment. Overall the data support a continuous model of processing, one in which programming and execution co-occur, and can do so without time cost

Interactive Exploration of Multitask Dependency Networks

Scientists increasingly depend on machine learning algorithms to discover patterns in complex data. Two examples addressed in this dissertation are identifying how information sharing among regions of the brain develops due to learning; and, learning dependency networks of blood proteins associated with cancer. Dependency networks, or graphical models, are learned from the observed data in order to make comparisons between the sub-populations of the dataset. Rarely is there sufficient data to infer robust individual networks for each sub-population. The multiple networks must be considered simultaneously; exploding the hypothesis space of the learning problem. Exploring this complex solution space requires input from the domain scientist to refine the objective function. This dissertation introduces a framework to incorporate domain knowledge in transfer learning to facilitate the exploration of solutions. The framework is a generalization of existing algorithms for multiple network structure identification. Solutions produced with human input narrow down the variance of solutions to those that answer questions of interest to domain scientists. Patterns, such as identifying differences between networks, are learned with higher confidence using transfer learning than through the standard method of bootstrapping. Transfer learning may be the ideal method for making comparisons among dependency networks, whether looking for similarities or differences. Domain knowledge input and visualization of solutions are combined in an interactive tool that enables domain scientists to explore the space of solutions efficiently

Nanoindentation of hydrated materials and tissues

Nanoindentation techniques have recently been adapted for the study of hydrated materials, including biological materials and hydrogels. There are unique challenges associated with handling and testing hydrated samples. For hydrated materials, a poroelastic or poroviscoelastic analysis, which explicitly treats the fluid flow through the porous material, is used to extract material properties from experimental data. Some key results from recent works using nanoindentation to evaluate hydrated materials are reviewed in the context of these challenges. Finally, as these studies represent relatively recent developments in the nanoindentation field, an outlook for the future is presented, in which it is clear that a consensus is emerging for quantitative evaluation of hydrated materials via a modified nanoindentation approach.This is the accepted manuscript. The final version is available at http://www.sciencedirect.com/science/article/pii/S1359028615000236

Influence of hydraulic resistance on flow features in an open channel confluence

A numerical model based on the 3D shallow water equations is set up for a 90° angle open channel confluence. The model is first calibrated and validated using experimental data by (Shumate, 1998). Then a series of numerical simulations is carried out, systematically increasing the friction coefficient, in order to investigate the impact of hydraulic resistance on the flow features in an open channel confluence. The properties of the separation zone (width and length) are found to be substantially altered by the hydraulic resistance. The hydrodynamic processes are analysed zooming in onto lateral momentum fluxes