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

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

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

Synthetic aperture radar target simulator

A simulator for simulating the radar return, or echo, from a target seen by a SAR antenna mounted on a platform moving with respect to the target is described. It includes a first-in first-out memory which has digital information clocked in at a rate related to the frequency of a transmitted radar signal and digital information clocked out with a fixed delay defining range between the SAR and the simulated target, and at a rate related to the frequency of the return signal. An RF input signal having a frequency similar to that utilized by a synthetic aperture array radar is mixed with a local oscillator signal to provide a first baseband signal having a frequency considerably lower than that of the RF input signal

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

SEASAT synthetic-aperture radar data user's manual

The SEASAT Synthetic-Aperture Radar (SAR) system, the data processors, the extent of the image data set, and the means by which a user obtains this data are described and the data quality is evaluated. The user is alerted to some potential problems with the existing volume of SEASAT SAR image data, and allows him to modify his use of that data accordingly. Secondly, the manual focuses on the ultimate focuses on the ultimate capabilities of the raw data set and evaluates the potential of this data for processing into accurately located, amplitude-calibrated imagery of high resolution. This allows the user to decide whether his needs require special-purpose data processing of the SAR raw data

Filling of the Mott-Hubbard gap in the high temperature photoemission spectrum of (V_0.972Cr_0.028)_2O_3

Photoemission spectra of the paramagnetic insulating (PI) phase of (V_0.972Cr_0.028)_2O_3, taken in ultra high vacuum up to the unusually high temperature (T) of 800 K, reveal a property unique to the Mott-Hubbard (MH) insulator and not observed previously. With increasing T the MH gap is filled by spectral weight transfer, in qualitative agreement with high-T theoretical calculations combining dynamical mean field theory and band theory in the local density approximation.Comment: 4 pages, 4 figure

Dynamical Mean-Field Theory for Molecular Electronics: Electronic Structure and Transport Properties

We present an approach for calculating the electronic structure and transport properties of nanoscopic conductors that takes into account the dynamical correlations of strongly interacting d- or f-electrons by combining density functional theory calculations with the dynamical mean-field theory. While the density functional calculation yields a static mean-field description of the weakly interacting electrons, the dynamical mean-field theory explicitly takes into account the dynamical correlations of the strongly interacting d- or f-electrons of transition metal atoms. As an example we calculate the electronic structure and conductance of Ni nanocontacts between Cu electrodes. We find that the dynamical correlations of the Ni 3d-electrons give rise to quasi-particle resonances at the Fermi-level in the spectral density. The quasi-particle resonances in turn lead to Fano lineshapes in the conductance characteristics of the nanocontacts similar to those measured in recent experiments of magnetic nanocontacts.Comment: replaced with revised version; 11 pages; 9 figure