12,665 research outputs found
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 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
VO -- 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 -orbital.Comment: 2 pages, 3 figures, SCES'04 conference proceeding
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
Recommended from our members
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
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
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