Spin-Flavor Separation and Non-Fermi Liquid Behavior in the Multichannel Kondo Problem: A Large N Approach

We consider a $SU(N)\times SU(M)$ generalization of the multichannel single-impurity Kondo model which we solve analytically in the limit $N\rightarrow \infty$, $M\rightarrow\infty$, with $\gamma=M/N$ fixed. Non-Fermi liquid behavior of the single electron Green function and of the local spin and flavor susceptibilities occurs in both regimes, $N\le M$ and $N > M$, with leading critical exponents {\em identical} to those found in the conformal field theory solution for {\em all} $N$ and $M$ (with $M\ge 2$). We explain this remarkable agreement and connect it to ``spin-flavor separation", the essential feature of the non-Fermi-liquid fixed point of the multichannel Kondo problem.Comment: 14 pages, 1 Figure (Poscript file attached), Revte

Design and develop a MOS magnetic memory Final report, 11 Mar. - 11 Sep. 1966

Interface problems between plated wire magnetic memory and MO

Friction and wear of oxide-ceramic sliding against IN-718 nickel base alloy at 25 to 800 C in atmospheric air

The friction and wear of oxide-ceramics sliding against the nickel base alloy IN-718 at 25 to 800 C were measured. The oxide materials tested were mullite (3Al2O3.2SiO2); lithium aluminum silicate (LiAlSi(x)O(y)); polycrystalline monolithic alpha alumina (alpha-Al2O3); single crystal alpha-Al2O3 (sapphire); zirconia (ZrO2); and silicon carbide (SiC) whisker-reinforced Al2O3 composites. At 25 C the mullite and zirconia had the lowest friction and the polycrystalline monolithic alumina had the lowest wear. At 800 C the Al2O3-8 vol/percent SiC whisker composite had the lowest friction and the Al2O3-25 vol/percent SiC composite had the lowest wear. The friction of the Al2O3-SiC whisker composites increased with increased whisker content while the wear decreased. In general, the wear-resistance of the ceramics improve with their hardness

Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails

Robots hold promise in many scenarios involving outdoor use, such as search-and-rescue, wildlife management, and collecting data to improve environment, climate, and weather forecasting. However, autonomous navigation of outdoor trails remains a challenging problem. Recent work has sought to address this issue using deep learning. Although this approach has achieved state-of-the-art results, the deep learning paradigm may be limited due to a reliance on large amounts of annotated training data. Collecting and curating training datasets may not be feasible or practical in many situations, especially as trail conditions may change due to seasonal weather variations, storms, and natural erosion. In this paper, we explore an approach to address this issue through virtual-to-real-world transfer learning using a variety of deep learning models trained to classify the direction of a trail in an image. Our approach utilizes synthetic data gathered from virtual environments for model training, bypassing the need to collect a large amount of real images of the outdoors. We validate our approach in three main ways. First, we demonstrate that our models achieve classification accuracies upwards of 95% on our synthetic data set. Next, we utilize our classification models in the control system of a simulated robot to demonstrate feasibility. Finally, we evaluate our models on real-world trail data and demonstrate the potential of virtual-to-real-world transfer learning.Comment: iROS 201

Friction and wear of sintered Alpha SiC sliding against IN-718 alloy at 25 to 800 C in atmospheric air at ambient pressure

The sliding friction and wear of the SiC-nickel based alloy IN-718 couple under line contact test conditions in atmospheric air at a linear velocity of 0.18 m/sec and a load of 6.8 kg (67N) was investigated at temperatures of 25 to 800 C. It was found that the coefficient of friction was 0.6 up to 350 C then decreased to 0.3 at 500 and 800 C. It is suggested that the sharp decrease in the friction in the range of 350 to 550 C is due to the lubrication value of oxidation products. The wear rate reaches a minimum of 1 x 10 to the -10 to 2 x 10 to the -10 cu cm/cm/kg at 400 to 600 C

The Neutrino Suppression Scale from Large Volumes

We present an argument in which the scale ~ 0.1 eV associated with neutrino masses naturally appears in a a class of (very) large volume compactifications, being tied to a supersymmetry scale of 10^3 GeV and a string scale of 10^11 GeV. The masses are of Majorana type and there is no right-handed neutrino within the low-energy field theory. The suppression scale 10^14 GeV is independent of the masses of the heavy states that are integrated out. These kind of constructions appear naturally in Type IIB flux compactifications. However, the arguments that lead to this result rely only on a few geometrical features of the compactification manifold, and hence can be used independently of string theory.Comment: 4 pages, RevTeX; v2. matches journal versio