Building a Holistic ATM Model for Future KPI Trade-Offs

We present the model developed within the Vista project, studying the future evolution of trade-offs between Key Performance Indicators. The model has a very broad scope and aims to simulate the changes that business and regulatory forces have at a strategic, pre-tactical and tactical level. The relevant factors that will affect the air transportation system are presented, as well as the scenarios to be simulated. The overall architecture of the model is described and a more detailed presentation of the economic component of the model is given. Some preliminary results of this part of the model illustrate its main mechanisms and capabilities

Discreteness and entropic fluctuations in GREM-like systems

Within generalized random energy models, we study the effects of energy discreteness and of entropy extensivity in the low temperature phase. At zero temperature, discreteness of the energy induces replica symmetry breaking, in contrast to the continuous case where the ground state is unique. However, when the ground state energy has an extensive entropy, the distribution of overlaps P(q) instead tends towards a single delta function in the large volume limit. Considering now the whole frozen phase, we find that P(q) varies continuously with temperature, and that state-to-state fluctuations of entropy wash out the differences between the discrete and continuous energy models.Comment: 7 pages, 3 figure, 2 figures are added, the volume changes from 4 pages to 7 page

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

Analysis of Kapitza-Dirac diffraction patterns beyond the Raman-Nath regime

We study Kapitza-Dirac diffraction of a Bose-Einstein condensate from a standing light wave for a square pulse with variable pulse length but constant pulse area. We find that for sufficiently weak pulses, the usual analytical short-pulse prediction for the Raman-Nath regime continues to hold for longer times, albeit with a reduction of the apparent modulation depth of the standing wave. We quantitatively relate this effect to the Fourier width of the pulse, and draw analogies to the Rabi dynamics of a coupled two-state system. Our findings, combined with numerical modeling for stronger pulses, are of practical interest for the calibration of optical lattices in ultracold atomic systems

Population stability: regulating size in the presence of an adversary

We introduce a new coordination problem in distributed computing that we call the population stability problem. A system of agents each with limited memory and communication, as well as the ability to replicate and self-destruct, is subjected to attacks by a worst-case adversary that can at a bounded rate (1) delete agents chosen arbitrarily and (2) insert additional agents with arbitrary initial state into the system. The goal is perpetually to maintain a population whose size is within a constant factor of the target size $N$. The problem is inspired by the ability of complex biological systems composed of a multitude of memory-limited individual cells to maintain a stable population size in an adverse environment. Such biological mechanisms allow organisms to heal after trauma or to recover from excessive cell proliferation caused by inflammation, disease, or normal development. We present a population stability protocol in a communication model that is a synchronous variant of the population model of Angluin et al. In each round, pairs of agents selected at random meet and exchange messages, where at least a constant fraction of agents is matched in each round. Our protocol uses three-bit messages and $\omega(\log^2 N)$ states per agent. We emphasize that our protocol can handle an adversary that can both insert and delete agents, a setting in which existing approximate counting techniques do not seem to apply. The protocol relies on a novel coloring strategy in which the population size is encoded in the variance of the distribution of colors. Individual agents can locally obtain a weak estimate of the population size by sampling from the distribution, and make individual decisions that robustly maintain a stable global population size

Must naive realists be relationalists?

Relationalism maintains that perceptual experience involves, as part of its nature, a distinctive kind of conscious perceptual relation between a subject of experience and an object of experience. Together with the claim that perceptual experience is presentational, relationalism is widely believed to be a core aspect of the naive realist outlook on perception. This is a mistake. I argue that naive realism about perception can be upheld without a commitment to relationalism

Innovator resilience potential: A process perspective of individual resilience as influenced by innovation project termination

Innovation projects fail at an astonishing rate. Yet, the negative effects of innovation project failures on the team members of these projects have been largely neglected in research streams that deal with innovation project failures. After such setbacks, it is vital to maintain or even strengthen project members’ innovative capabilities for subsequent innovation projects. For this, the concept of resilience, i.e. project members’ potential to positively adjust (or even grow) after a setback such as an innovation project failure, is fundamental. We develop the second-order construct of innovator resilience potential, which consists of six components – self-efficacy, outcome expectancy, optimism, hope, self-esteem, and risk propensity – that are important for project members’ potential of innovative functioning in innovation projects subsequent to a failure. We illustrate our theoretical findings by means of a qualitative study of a terminated large-scale innovation project, and derive implications for research and management

Neurology

Contains reports on four research projects.U. S. Public Health Service (B-3055-4)U. S. Public Health Service (B-3090-4)U. S. Public Health Service (MH-06175-02)U.S. Navy (Office of Naval Research (Nonr-1841 (70))U. S. Air Force (AF49(638)-1313

Simplest random K-satisfiability problem

We study a simple and exactly solvable model for the generation of random satisfiability problems. These consist of $\gamma N$ random boolean constraints which are to be satisfied simultaneously by $N$ logical variables. In statistical-mechanics language, the considered model can be seen as a diluted p-spin model at zero temperature. While such problems become extraordinarily hard to solve by local search methods in a large region of the parameter space, still at least one solution may be superimposed by construction. The statistical properties of the model can be studied exactly by the replica method and each single instance can be analyzed in polynomial time by a simple global solution method. The geometrical/topological structures responsible for dynamic and static phase transitions as well as for the onset of computational complexity in local search method are thoroughly analyzed. Numerical analysis on very large samples allows for a precise characterization of the critical scaling behaviour.Comment: 14 pages, 5 figures, to appear in Phys. Rev. E (Feb 2001). v2: minor errors and references correcte