Isospectral flow in Loop Algebras and Quasiperiodic Solutions of the Sine-Gordon Equation

The sine-Gordon equation is considered in the hamiltonian framework provided by the Adler-Kostant-Symes theorem. The phase space, a finite dimensional coadjoint orbit in the dual space \grg^* of a loop algebra \grg, is parametrized by a finite dimensional symplectic vector space $W$ embedded into \grg^* by a moment map. Real quasiperiodic solutions are computed in terms of theta functions using a Liouville generating function which generates a canonical transformation to linear coordinates on the Jacobi variety of a suitable hyperelliptic curve.Comment: 12 pg

Goal pursuit during the three stages of the migration process

Migration poses a strong contextual change for individuals and it necessitates the adjustment of goals and aspirations. Although goal-related processes seem highly relevant to migration success (e.g., migrant well-being and adjustment), existing research in the area is scattered and lacks an overarching theoretical framework. By systematically analyzing the current literature on goal pursuit in the migration context, we aim to give an overview of the current state of the field, identify areas that need further research attention, and recommend alternative methodological approaches for future studies. This systematic literature review uses the different stages of the migration process (pre-migration, during migration, and potential repatriation or onward migration) and the three different goal facets (goal structure, goal process, and goal content) as an organizing framework. Our discussion focuses on the theoretical and methodological implications of our findings. The article demonstrates the need for further research in the field of goal pursuit in the migration context

Motivation in words : promotion- and prevention-oriented leader communication in times of crisis.

Research demonstrates that situational uncertainty or crisis strongly influences the endorsement of the more charismatic or decisive leadership styles and that inspirational communication is at the heart of these styles. However, there is currently little understanding of what leaders should convey through their communication to be endorsed in crisis. Based on regulatory focus theory, we argue that times of crisis make leaders who use more promotion-oriented communication more likely to be endorsed and leaders who use more prevention-oriented communication less likely to be endorsed. Results of Study 1, an archival study of U.S. presidents, show that presidents who use more promotion-oriented communication are more endorsed but only if economic growth is low or if inflation is high, while no effects of the use of prevention orientation of communication surfaces. Results of Study 2, a laboratory experiment, show that leaders who communicate a promotion orientation, as compared to a prevention orientation, motivate higher performance in participants in a crisis condition, but that there is no difference in a no-crisis (i.e. control) condition. Finally, results of Study 3, a scenario experiment, demonstrate that organizational leaders that communicate more promotion-oriented (as opposed to more prevention-oriented) have a higher chance of being endorsed but only in times of crisis and that this effect is mediated by followers’ motivation to realize the plans of the leader

Prediction of stable walking for a toy that cannot stand

Previous experiments [M. J. Coleman and A. Ruina, Phys. Rev. Lett. 80, 3658 (1998)] showed that a gravity-powered toy with no control and which has no statically stable near-standing configurations can walk stably. We show here that a simple rigid-body statically-unstable mathematical model based loosely on the physical toy can predict stable limit-cycle walking motions. These calculations add to the repertoire of rigid-body mechanism behaviors as well as further implicating passive-dynamics as a possible contributor to stability of animal motions.Comment: Note: only corrections so far have been fixing typo's in these comments. 3 pages, 2 eps figures, uses epsf.tex, revtex.sty, amsfonts.sty, aps.sty, aps10.sty, prabib.sty; Accepted for publication in Phys. Rev. E. 4/9/2001 ; information about Andy Ruina's lab (including Coleman's, Garcia's and Ruina's other publications and associated video clips) can be found at: http://www.tam.cornell.edu/~ruina/hplab/index.html and more about Georg Bock's Simulation Group with whom Katja Mombaur is affiliated can be found at http://www.iwr.uni-heidelberg.de/~agboc

The radial pulsation of AI Aurigae

We present an analysis of eleven years of Stromgren by photometry of the red semiregular variable star AI Aurigae. An early period determination of 63.9 days is confirmed by the long-term light curve behaviour. The light curve shows semi-regular changes with a mean period of 65 days reaching an amplitude of 0.6 mag in some cycles. The b-y colour changes perfectly parallel the V light curve, suggesting radial oscillation to be the main reason for the observed variations. We estimate the main characteristics of the star (mass, radius, effective temperature) that suggest radial pulsation in fundamental or first overtone mode.Comment: 7 pages, 3 figures, accepted for publication in A&

Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI

Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical trials, estimation of brain progressive rates can be applied to track therapeutic efficacy of disease modifying treatments. However, most state-of-the-art measurements calculate changes directly by segmentation and/or deformable registration of MRI images, and may misreport head motion or MRI artifacts as neurodegeneration, impacting their accuracy. In our previous study, we developed a deep learning method DeepAtrophy that uses a convolutional neural network to quantify differences between longitudinal MRI scan pairs that are associated with time. DeepAtrophy has high accuracy in inferring temporal information from longitudinal MRI scans, such as temporal order or relative inter-scan interval. DeepAtrophy also provides an overall atrophy score that was shown to perform well as a potential biomarker of disease progression and treatment efficacy. However, DeepAtrophy is not interpretable, and it is unclear what changes in the MRI contribute to progression measurements. In this paper, we propose Regional Deep Atrophy (RDA), which combines the temporal inference approach from DeepAtrophy with a deformable registration neural network and attention mechanism that highlights regions in the MRI image where longitudinal changes are contributing to temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring in clinical trials of early AD.Comment: Submitted to NeuroImage for revie