Microscopic conditions favoring itinerant ferromagnetism: Hund's rule coupling and orbital degeneracy

The importance of Hund's rule coupling for the stabilization of itinerant ferromagnetism is investigated within a two-band Hubbard model. The magnetic phase diagram is calculated by finite-temperature quantum Monte Carlo simulations within the dynamical mean-field theory. Ferromagnetism is found in a broad range of electron fillings whereas antiferromagnetism exists only near half filling. The possibility of orbital ordering at quarter filling is also analyzed.Comment: 5 pages, 6 figures, RevTeX, final version contains an additional phase diagram for smaller Hund's rule coupling. to appear in Eur. Phys. J. B (1998

Correlated-Electron Theory of Strongly Anisotropic Metamagnets

We present the first correlated-electron theory of metamagnetism in strongly anisotropic antiferromagnets. Quantum-Monte-Carlo techniques are used to calculate the field vs. temperature phase diagram of the infinite-dimensional Hubbard model with easy axis. A metamagnetic transition scenario with 1.~order and 2.~order phase transitions is found. The apparent similarities to the phase diagram of FeBr$_2$ and to mean-field results for the Ising model with competing interactions are discussed.Comment: 4 pages, RevTeX + one uuencoded ps-file including 3 figure

Transgenic expression of the Ly49A natural killer cell receptor confers class I major histocompatibility complex (MHC)-specific inhibition and prevents bone marrow allograft rejection.

Natural killer (NK) cells and some T cells are endowed with receptors specific for class I major histocompatibility complex (MHC) molecules that can inhibit cellular effector functions. The function of the Ly49 receptor family has been studied in vitro, but no gene transfer experiments have directly established the role of these receptors in NK cell functions. We show here that transgenic expression of the H-2Dd-specific Ly49A receptor in all NK cells and T cells conferred class I-specific inhibition of NK cell-mediated target cell lysis as well as of T cell proliferation. Furthermore, transgene expression prevented NK cell-mediated rejection of allogeneic H-2d bone marrow grafts by irradiated mice. These results demonstrate the function and specificity of Ly49 receptors in vivo, and establish that their subset-specific expression is necessary for the discrimination of MHC-different cells by NK cells in unmanipulated mice

Double Exchange model for nanoscopic clusters

We solve the double exchange model on nanoscopic clusters exactly, and specifically consider a six-site benzene-like nanocluster. This simple model is an ideal testbed for studying magnetism in nanoclusters and for validating approximations such as the dynamical mean field theory (DMFT). Non-local correlations arise between neighboring localized spins due to the Hund's rule coupling, favoring a short-range magnetic order of ferro- or antiferromagnetic type. For a geometry with more neighboring sites or a sufficiently strong hybridization between leads and the nanocluster, these non-local correlations are less relevant, and DMFT can be applied reliably.Comment: 9 pages, 9 figures, 1 tabl

NASA/JPL Aircraft SAR Workshop Proceedings

Speaker-supplied summaries of the talks given at the NASA/JPL Aircraft SAR Workshop on February 4 and 5, 1985, are provided. These talks dealt mostly with composite quadpolarization imagery from a geologic or ecologic prespective. An overview and summary of the system characteristics of the L-band synthetic aperture radar (SAR) flown on the NASA CV-990 aircraft are included as supplementary information. Other topics ranging from phase imagery and interferometric techniques classifications of specific areas, and the potentials and limitations of SAR imagery in various applications are discussed

Two Aspects of the Mott-Hubbard Transition in Cr-doped V_2O_3

The combination of bandstructure theory in the local density approximation with dynamical mean field theory was recently successfully applied to V$_2$O$_3$ -- a material which undergoes the f amous Mott-Hubbard metal-insulator transition upon Cr doping. The aim of this sh ort paper is to emphasize two aspects of our recent results: (i) the filling of the Mott-Hubbard gap with increasing temperature, and (ii) the peculiarities of the Mott-Hubbard transition in this system which is not characterized by a diver gence of the effective mass for the $a_{1g}$-orbital.Comment: 2 pages, 3 figures, SCES'04 conference proceeding

Review of Session 7: non-cancer risk

Astronauts in space and cancer patients being treated with ion beam radiotherapy can be exposed to charged particle radiations including energetic protons and heavy ions. These charged particles may be more effective than photons in inducing cancer as well as in causing non-cancer effects. The latter include acute damage from large solar particle events to the blood-forming organs and skin, acute and (from heavier ions) late damage to the central nervous system, and late degenerative damage to the lens of the eye and the cardiovascular, circulatory and respiratory systems. The presentations in this session discussed a number of non-cancer effects of protons and heavier charged particles including acute hematopoietic alterations, potentially detrimental cardiovascular and circulatory effects, and lifespan shortening

Enabling Robots to Communicate their Objectives

The overarching goal of this work is to efficiently enable end-users to correctly anticipate a robot's behavior in novel situations. Since a robot's behavior is often a direct result of its underlying objective function, our insight is that end-users need to have an accurate mental model of this objective function in order to understand and predict what the robot will do. While people naturally develop such a mental model over time through observing the robot act, this familiarization process may be lengthy. Our approach reduces this time by having the robot model how people infer objectives from observed behavior, and then it selects those behaviors that are maximally informative. The problem of computing a posterior over objectives from observed behavior is known as Inverse Reinforcement Learning (IRL), and has been applied to robots learning human objectives. We consider the problem where the roles of human and robot are swapped. Our main contribution is to recognize that unlike robots, humans will not be exact in their IRL inference. We thus introduce two factors to define candidate approximate-inference models for human learning in this setting, and analyze them in a user study in the autonomous driving domain. We show that certain approximate-inference models lead to the robot generating example behaviors that better enable users to anticipate what it will do in novel situations. Our results also suggest, however, that additional research is needed in modeling how humans extrapolate from examples of robot behavior.Comment: RSS 201

The invisible power of fairness. How machine learning shapes democracy

Many machine learning systems make extensive use of large amounts of data regarding human behaviors. Several researchers have found various discriminatory practices related to the use of human-related machine learning systems, for example in the field of criminal justice, credit scoring and advertising. Fair machine learning is therefore emerging as a new field of study to mitigate biases that are inadvertently incorporated into algorithms. Data scientists and computer engineers are making various efforts to provide definitions of fairness. In this paper, we provide an overview of the most widespread definitions of fairness in the field of machine learning, arguing that the ideas highlighting each formalization are closely related to different ideas of justice and to different interpretations of democracy embedded in our culture. This work intends to analyze the definitions of fairness that have been proposed to date to interpret the underlying criteria and to relate them to different ideas of democracy.Comment: 12 pages, 1 figure, preprint version, submitted to The 32nd Canadian Conference on Artificial Intelligence that will take place in Kingston, Ontario, May 28 to May 31, 201