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Goda och tydliga karriärvägar vid Lunds universitet : Rapport
EU Labour Law in Flux – Hard, Soft or Fundamental? : Keynote paper for the 11th ILERA European Regional Congress, Milan, 8–10 September 2016
High Precision Mean Field Results for Lattice Gauge Theories : with special attention paid to the gauge dependence of mean-field perturbation theory to finite order
We do mean-field perturbation theory for U(1) lattice gauge theory in the axial gauge, and evaluate corrections from fluctuations up to fourth order for the free energy and plaquette energy. Comparing with similar results previously obtained in the Feynman gauge we find, to those orders studied, a gauge dependence of the size of the first correction term neglected with one exception. This gauge de
Scaling laws for mode locking in circle maps
The self-similar structure of mode lockings for circle maps is studied by means of the associated Farey trees. We investigate numerically several classes of scaling relations implicit in the Farey organization of mode lockings and discuss the extent to which they lead to universal scaling laws.
Hamilton-Jacobi Theory for the Massless Relativistic String
A Probability Measure on Parton and String States
Topology in Twisted Lattice Gauge Theories
Finite Size Effects and Twisted Boundary Conditions
We explore the possibility of reducing finite size effects in the weak coupling region of glueball correlations and Wilson loops. Our analysis indicates that twisted boundary conditions do diminish finite size effects and numerical evidence for this is given for the glueball correlation.
Ising Spins on a Dynamically Triangulated Random Surface
We study a model of random surfaces endowed with fermionic degrees of freedom. The critical parameters are estimated using a Monte Carlo simulation method, for the dimensionality d of the embedding space ranging from d=0 to d=10.
A New Method for Mapping Optimization Problems onto Neural Networks
A novel modified method for obtaining approximate solutions to difficult optimization problems within the neural network paradigm is presented. We consider the graph partition and the travelling salesman problems. The key new ingredient is a reduction of solution space by one dimension by using graded neurons, thereby avoiding the destructive redundancy that has plagued these problems when using s
Teachers and Classes with Neural Networks
A convenient mapping and an efficient algorithm for solving scheduling problems within the neural network paradigm is presented. It is based on a reduced encoding scheme and a mean field annealing prescription which was recently successfully applied to TSP.Most scheduling problems are characterized by a set of hard and soft constraints. The prime target of this work is the hard constraints. In thi
Complex Scheduling with Potts Neural Networks
In a recent paper (Gislén et al. 1989) a convenient encoding and an efficient mean field algorithm for solving scheduling problems using a Potts neural network was developed and numerically explored on simplified and synthetic problems. In this work the approach is extended to realistic applications both with respect to problem complexity and size. This extension requires among other things the in
Random Surfaces with Ising Spins
Bosonic strings can be discretized in terms of dynamically triangulatedrandom surfaces. We investigate the possibility of introducing fermionicdegrees of freedom on the surface in terms of Ising spins, which in twodimensions correspond to Majorana fermions. Critical properties of themodel are estimated using finite-size scaling methods.
Hydraulic forces contribute to left ventricular diastolic filling
Myocardial active relaxation and restoring forces are known determinants of left ventricular (LV) diastolic function. We hypothesize the existence of an additional mechanism involved in LV filling, namely, a hydraulic force contributing to the longitudinal motion of the atrioventricular (AV) plane. A prerequisite for the presence of a net hydraulic force during diastole is that the atrial short-ax
Functional Contribution of Circumferential Versus Longitudinal Strain : Different Concepts Suggest Conflicting Results
Rotor Neurons: Basic Formalism and Dynamics
Rotor neurons are introduced to encode states living on the surface of a sphere in D dimensions. Such rotors can be regarded as continuous generalizations of binary (Ising) neurons. The corresponding mean field equations are derived, and phase transition properties based on linearized dynamics are given. The power of this approach is illustrated with an optimization problem—placing N identical cha
Response by Jablonowski et al to Letter Regarding Article, "Cardiovascular Magnetic Resonance to Predict Appropriate Implantable Cardioverter Defibrillator Therapy in Ischemic and Nonischemic Cardiomyopathy Patients Using Late Gadolinium Enhancement Border Zone : Comparison of Four Analysis Methods"
Optimization with Potts Neural Networks
The Potts Neural Network approach to non-binary discrete optimizationproblems is described. It applies to problems that can be described asa set of elementary 'multiple choice' options. Instead of the conventionalbinary (Ising) neurons, mean field Potts neurons, having several availablestates, are used to describe the elementary degrees of freedom of suchproblems. The dynamics consists of iteratin
Combinatorial Optimization with Neural Networks
A general introduction to the use of feed-back artificial neural networks (ANN) for obtaining good approximate solutions to combinatorial optimization problems is given, assuming no previous knowledge in the field. In particular we emphasize a novel neural mapping technique which efficiently reduces the solution space. This approach maps the problems onto Potts glass rather than spin glass models.