In-medium Pion-Pion Interaction and Chiral Symmetry Restoration

We discuss medium modifications of the unitarized pion-pion interaction in the nuclear medium. We incorporate both the effects of chiral symmetry restoration and the influence of collective nuclear pionic modes, originating from the p-wave coupling of the pion to delta-hole configurations. We show how the resulting strong enhancement of the sigma-meson spectral function is related to large fluctuations of the condensate associated with the partial restoration of chiral symmetry.Comment: 8 pages, 3 Postscript figures, contribution to the `International Workshop XXVIII on Gross Properties of Nuclei and Nuclear Excitations', Hirschegg, Austria, January 16-22 200

Unified theory of bound and scattering molecular Rydberg states as quantum maps

Using a representation of multichannel quantum defect theory in terms of a quantum Poincar\'e map for bound Rydberg molecules, we apply Jung's scattering map to derive a generalized quantum map, that includes the continuum. We show, that this representation not only simplifies the understanding of the method, but moreover produces considerable numerical advantages. Finally we show under what circumstances the usual semi-classical approximations yield satisfactory results. In particular we see that singularities that cause problems in semi-classics are irrelevant to the quantum map

First-Order Model Checking on Generalisations of Pushdown Graphs

We study the first-order model checking problem on two generalisations of pushdown graphs. The first class is the class of nested pushdown trees. The other is the class of collapsible pushdown graphs. Our main results are the following. First-order logic with reachability is uniformly decidable on nested pushdown trees. Considering first-order logic without reachability, we prove decidability in doubly exponential alternating time with linearly many alternations. First-order logic with regular reachability predicates is uniformly decidable on level 2 collapsible pushdown graphs. Moreover, nested pushdown trees are first-order interpretable in collapsible pushdown graphs of level 2. This interpretation can be extended to an interpretation of the class of higher-order nested pushdown trees in the collapsible pushdown graph hierarchy. We prove that the second level of this new hierarchy of nested trees has decidable first-order model checking. Our decidability result for collapsible pushdown graph relies on the fact that level 2 collapsible pushdown graphs are uniform tree-automatic. Our last result concerns tree-automatic structures in general. We prove that first-order logic extended by Ramsey quantifiers is decidable on all tree-automatic structures.Comment: phd thesis, 255 page

Ductile Metallic Glasses in Supercooled Martensitic Alloys

We report ductile bulk metallic glasses based on martensitic alloys. The slowly cooled specimens contain a mixture of parent 'austenite' and martensite phase. The slightly faster cooled bulk metallic glasses with 2-5 nm sized 'austenite'-like crystalline cluster reveal high strength and large ductility (16%). Shear bands propagate in a slither mode in this spatially inhomogeneous glassy structure and undergo considerable 'thickening' from 5-25 nm. A 'stress induced displacive transformation' is proposed to be responsible for both plasticity and work-hardening-like behavior of these 'M-Glasses'

Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models

Anticipation is crucial for fluent human-robot interaction, which allows a robot to independently coordinate its actions with human beings in joint activities. An anticipatory robot relies on a predictive model of its human partners, and selects its own action according to the model's predictions. Intention inference and decision making are key elements towards such anticipatory robots. In this thesis, we present a machine-learning approach to intention inference and decision making, based on Hierarchical Gaussian Process Dynamics Models (H-GPDMs). We first introduce the H-GPDM, a class of generic latent-variable dynamics models. The H-GPDM represents the generative process of complex human movements that are directed by exogenous driving factors. Incorporating the exogenous variables in the dynamics model, the H-GPDM achieves improved interpretation, analysis, and prediction of human movements. While exact inference of the exogenous variables and the latent states is intractable, we introduce an approximate method using variational Bayesian inference, and demonstrate the merits of the H-GPDM in three different applications of human movement analysis. The H-GPDM lays a foundation for the following studies on intention inference and decision making. Intention inference is an essential step towards anticipatory robots. For this purpose, we consider a special case of the H-GPDM, the Intention-Driven Dynamics Model (IDDM), which considers the human partners' intention as exogenous driving factors. The IDDM is applicable to intention inference from observed movements using Bayes' theorem, where the latent state variables are marginalized out. As most robotics applications are subject to real-time constraints, we introduce an efficient online algorithm that allows for real-time intention inference. We show that the IDDM achieved state-of-the-art performance in intention inference using two human-robot interaction scenarios, i.e., target prediction for robot table tennis and action recognition for interactive robots. Decision making based on a time series of predictions allows a robot to be proactive in its action selection, which involves a trade-off between the accuracy and confidence of the prediction and the time for executing a selected action. To address the problem of action selection and optimal timing for initiating the movement, we formulate the anticipatory action selection using Partially Observable Markov Decision Process, where the H-GPDM is adopted to update belief state and to estimate transition model. We present two approaches to policy learning and decision making, and show their effectiveness using human-robot table tennis. In addition, we consider decision making solely based on the preference of the human partners, where observations are not sufficient for reliable intention inference. We formulate it as a repeated game and present a learning approach to safe strategies that exploit the humans' preferences. The learned strategy enables action selection when reliable intention inference is not available due to insufficient observation, e.g., for a robot to return served balls from a human table tennis player. In this thesis, we use human-robot table tennis as a running example, where a key bottleneck is the limited amount of time for executing a hitting movement. Movement initiation usually requires an early decision on the type of action, such as a forehand or backhand hitting movement, at least 80ms before the opponent has hit the ball. The robot, therefore, needs to be anticipatory and proactive of the opponent's intended target. Using the proposed methods, the robot can predict the intended target of the opponent and initiate an appropriate hitting movement according to the prediction. Experimental results show that the proposed intention inference and decision making methods can substantially enhance the capability of the robot table tennis player, using both a physically realistic simulation and a real Barrett WAM robot arm with seven degrees of freedom

IMPACT OF SIMPLIFIED STATIONARY CAVITY BEAM LOADING ON THE LONGITUDINAL FEEDBACK SYSTEM FOR SIS100 *

Abstract The main synchrotron SIS100 of the Facility for Antiproton and Ion Research (FAIR) will be equipped with a bunchby-bunch feedback system to damp longitudinal beam oscillations. In the basic layout, one three-tap finite impulse response (FIR) filter will be used for each single bunch and oscillation mode. The detected oscillations are used to generate a correction voltage in dedicated broadband radio frequency (RF) cavities. The digital filter is completely described by two parameters, the feedback gain and the passband center frequency, which have to be defined depending on the longitudinal beam dynamics. In earlier works BEAM LOADING were chosen in quadrature with the first harmonic of the beam current as sketched i

Global services and support for children with developmental delays and disabilities: Bridging research and policy gaps

Summary: 1. The United Nations Sustainable Development Goals and the UN Convention on the Rights of the Child (CRC) envision an inclusive society in which health and education contribute to the well-being of all. To achieve this vision, children with developmental delays and behavioral, cognitive, mental, and neurological disabilities need greater access to health care, early childhood care and development services, and education. 2. Improved population-level detection, alongside screening, assessment, and linkage to evidence-based, intersectoral services in the first years of life, can help maximize capabilities and increase the chances of social inclusion for children with developmental delays and disabilities. 3. Educational programs for children with delays and disabilities whose service delivery structure supports the ability of parents to work should be encouraged so that parents can participate in achieving children’s educational goals while also meeting their financial needs. 4. Parents and caregivers who receive training in psychosocial interventions and ongoing support can help children with delays and disabilities thrive in family contexts. 5. Family mental health influences the developmental trajectory of children. Ensuring that parents and caregivers have access to affordable, quality mental health services helps to prevent poor outcomes for children. 6. Rigorous evaluation, continuous quality improvement, and regular monitoring of the programmatic outcomes of services and policy approaches targeting children and caregivers would inform their implementation and serve to disseminate lessons learned from successful policy and program implementation