A description of the software analysis from flight and simulation data of the course cut limiter in the TCV b-737 area navigation computer

During automatic horizontal path captures, the (Terminal Configured Vehicle) B-737 airplane maintained smaller than designed path intercept angles and experienced a sawtooth bank angle oscillation during its turn towards the path. From flight data, it was determined that these anomalies were caused by the improper output of the course cut limiter in the horizontal path control law. The output from the course cut limiter did not obtain its full value and it was calculated stepwise discontinuously. The automatic horizontal path captures were then conducted on the TCV B-737 airplane real-time simulation. The path intercept angles were maintained properly and no bank angle oscillation was encountered. Data showed that the course cut limiter was calculated at its full value in a continuous manner. The intermediate calculations of the course cut limiter in the airplane's navigation computer were rewritten and rescaled in such a manner that truncation errors could be minimized. The horizontal path capture tests were then reflown. The airplane maintained the proper path intercept angle and no bank angle oscillations occurred on any of the tests

Gamma-ray emission associated with Cluster-scale AGN Outbursts

Recent observations have revealed the existence of enormously energetic ~10^61 erg AGN outbursts in three relatively distant galaxy clusters. These outbursts have produced bubbles in the intra-cluster medium, apparently supported by pressure from relativistic particles and/or magnetic fields. Here we argue that if > GeV particles are responsible then these particles are very likely protons and nuclei, rather than electrons, and that the gamma-ray emission from these objects, arising from the interactions of these hadrons in the intra-cluster medium, may be marginally detectable with instruments such as GLAST and HESS.Comment: 8 pages, 4 figures, accepted by MNRA

Fast Spectral Clustering Using Autoencoders and Landmarks

In this paper, we introduce an algorithm for performing spectral clustering efficiently. Spectral clustering is a powerful clustering algorithm that suffers from high computational complexity, due to eigen decomposition. In this work, we first build the adjacency matrix of the corresponding graph of the dataset. To build this matrix, we only consider a limited number of points, called landmarks, and compute the similarity of all data points with the landmarks. Then, we present a definition of the Laplacian matrix of the graph that enable us to perform eigen decomposition efficiently, using a deep autoencoder. The overall complexity of the algorithm for eigen decomposition is $O(np)$, where $n$ is the number of data points and $p$ is the number of landmarks. At last, we evaluate the performance of the algorithm in different experiments.Comment: 8 Pages- Accepted in 14th International Conference on Image Analysis and Recognitio

Global adaptation in networks of selfish components: emergent associative memory at the system scale

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. Such global functions within a single agent or organism are not wholly surprising since the mechanisms (e.g. Hebbian learning) that create these neural organisations may be selected for this purpose, but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviours when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g. when they can influence which other agents they interact with) then, in adapting these inter-agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviours as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalise by idealising stored patterns and/or creating new combinations of sub-patterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviours in the same sense, and by the same mechanism, as the organisational principles familiar in connectionist models of organismic learning

Bio-linguistic transition and Baldwin effect in an evolutionary naming-game model

We examine an evolutionary naming-game model where communicating agents are equipped with an evolutionarily selected learning ability. Such a coupling of biological and linguistic ingredients results in an abrupt transition: upon a small change of a model control parameter a poorly communicating group of linguistically unskilled agents transforms into almost perfectly communicating group with large learning abilities. When learning ability is kept fixed, the transition appears to be continuous. Genetic imprinting of the learning abilities proceeds via Baldwin effect: initially unskilled communicating agents learn a language and that creates a niche in which there is an evolutionary pressure for the increase of learning ability.Our model suggests that when linguistic (or cultural) processes became intensive enough, a transition took place where both linguistic performance and biological endowment of our species experienced an abrupt change that perhaps triggered the rapid expansion of human civilization.Comment: 7 pages, minor changes, accepted in Int.J.Mod.Phys.C, proceedings of Max Born Symp. Wroclaw (Poland), Sept. 2007. Java applet is available at http://spin.amu.edu.pl/~lipowski/biolin.html or http://www.amu.edu.pl/~lipowski/biolin.htm

Metallic phase of the quantum Hall effect in four-dimensional space

We study the phase diagram of the quantum Hall effect in four-dimensional (4D) space. Unlike in 2D, in 4D there exists a metallic as well as an insulating phase, depending on the disorder strength. The critical exponent $\nu\approx 1.2$ of the diverging localization length at the quantum Hall insulator-to-metal transition differs from the semiclassical value $\nu=1$ of 4D Anderson transitions in the presence of time-reversal symmetry. Our numerical analysis is based on a mapping of the 4D Hamiltonian onto a 1D dynamical system, providing a route towards the experimental realization of the 4D quantum Hall effect.Comment: 4+epsilon pages, 3 figure

Monoclonal Antibody Identification of Subpopulations of Cerebral Cortical Neurons Affected in Alzheimer disease

Neuronal degeneration is one of the hallmarks of Alzheimer disease (AD). Given the paucity of molecular markers available for the identification of neuronal subtypes, the specificity of neuronal loss within the cerebral cortex has been difficult to evaluate. With a panel of four monoclonal antibodies (mAbs) applied to central nervous system tissues from AD patients, we have immunocytochemically identified a population of vulnerable cortical neurons; a subpopulation of pyramidal neurons is recognized by mAbs 3F12 and 44.1 in the hippocampus and neocortex, and clusters of multipolar neurons in the entorhinal cortex reactive with mAb 44.1 show selective degeneration. Closely adjacent stellate-like neurons in these regions, identified by mAb 6A2, show striking preservation in AD. The neurons recognized by mAbs 3F12 and 44.1, to the best of our knowledge, do not comprise a single known neurotransmitter system. mAb 3A4 identifies a phosphorylated antigen that is undetectable in normal brain but accumulates early in the course of AD in somas of vulnerable neurons. Antigen 3A4 is distinct from material reactive with thioflavin S or antibody generated against paired helical filaments. Initially, antigen 3A4 is localized to neurons in the entorhinal cortex and subiculum, later in the association neocortex, and, ultimately in cases of long duration, in primary sensory cortical regions. mAb 3F12 recognizes multiple bands on immunoblots of homogenates of normal and Ad cortical tissues, whereas mAb 3A4 does not bind to immunoblots containing neurofilament proteins or brain homogenates from AD patients. Ultrastructurally, antigen 3A4 is localized to paired-helical filaments. Using these mAbs, further molecular characterization of the affected cortical neurons is now possible

Decaying dark matter: a stacking analysis of galaxy clusters to improve on current limits

We show that a stacking approach to galaxy clusters can improve current limits on decaying dark matter by a factor $\gtrsim 5-100$, with respect to a single source analysis, for all-sky instruments such as Fermi-LAT. Based on the largest sample of X-ray-selected galaxy clusters available to date (the MCXC meta-catalogue), we provide all the astrophysical information, in particular the astrophysical term for decaying dark matter, required to perform an analysis with current instruments.Comment: 6 pages, 3 figures, supplementary file available on demand, accepted for publication in PR