Non-collinear Magnetoelectronics

The electron transport properties of hybrid ferromagnetic|normal metal structures such as multilayers and spin valves depend on the relative orientation of the magnetization direction of the ferromagnetic elements. Whereas the contrast in the resistance for parallel and antiparallel magnetizations, the so-called Giant Magnetoresistance, is relatively well understood for quite some time, a coherent picture for non-collinear magnetoelectronic circuits and devices has evolved only recently. We review here such a theory for electron charge and spin transport with general magnetization directions that is based on the semiclassical concept of a vector spin accumulation. In conjunction with first-principles calculations of scattering matrices many phenomena, e.g. the current-induced spin-transfer torque, can be understood and predicted quantitatively for different material combinations.Comment: 163 pages, to be published in Physics Report

The glycolytic enzyme phosphofructokinase-1 assembles into filaments.

Despite abundant knowledge of the regulation and biochemistry of glycolytic enzymes, we have limited understanding on how they are spatially organized in the cell. Emerging evidence indicates that nonglycolytic metabolic enzymes regulating diverse pathways can assemble into polymers. We now show tetramer- and substrate-dependent filament assembly by phosphofructokinase-1 (PFK1), which is considered the "gatekeeper" of glycolysis because it catalyzes the step committing glucose to breakdown. Recombinant liver PFK1 (PFKL) isoform, but not platelet PFK1 (PFKP) or muscle PFK1 (PFKM) isoforms, assembles into filaments. Negative-stain electron micrographs reveal that filaments are apolar and made of stacked tetramers oriented with exposed catalytic sites positioned along the edge of the polymer. Electron micrographs and biochemical data with a PFKL/PFKP chimera indicate that the PFKL regulatory domain mediates filament assembly. Quantified live-cell imaging shows dynamic properties of localized PFKL puncta that are enriched at the plasma membrane. These findings reveal a new behavior of a key glycolytic enzyme with insights on spatial organization and isoform-specific glucose metabolism in cells

タービダイトにもとづいた混濁流の挙動の復元－安野層と日本海溝の例

京都大学新制・課程博士博士(理学)甲第24174号理博第4865号京都大学大学院理学研究科地球惑星科学専攻(主査)准教授 成瀬 元, 准教授 堤 昭人, 教授 野口 高明学位規則第4条第1項該当Doctor of ScienceKyoto UniversityDFA

Simulations of Infrared Radiances Over a Deep Convective Cloud System Observed During TC4: Potential for Enhancing Nocturnal Ice Cloud Retrievals

Retrievals of ice cloud properties using infrared measurements at 3.7, 6.7, 7.3, 8.5, 10.8, and 12.0 microns can provide consistent results regardless of solar illumination, but are limited to cloud optical thicknesses tau 20, the 3.7 - 10.8 microns and 3.7 - 6.7 microns BTDs are the most sensitive to D(sub e). Satellite imagery appears consistent with these results. Keywords: clouds; optical depth; particle size; satellite; TC4; multispectral thermal infrare

Automatic Configuration of Programmable Logic Controller Emulators

Programmable logic controllers (PLCs), which are used to control much of the world\u27s critical infrastructures, are highly vulnerable and exposed to the Internet. Many efforts have been undertaken to develop decoys, or honeypots, of these devices in order to characterize, attribute, or prevent attacks against Industrial Control Systems (ICS) networks. Unfortunately, since ICS devices typically are proprietary and unique, one emulation solution for a particular vendor\u27s model will not likely work on other devices. Many previous efforts have manually developed ICS honeypots, but it is a very time intensive process. Thus, a scalable solution is needed in order to automatically configure PLC emulators. The ScriptGenE Framework presented in this thesis leverages several techniques used in reverse engineering protocols in order to automatically configure PLC emulators using network traces. The accuracy, flexibility, and efficiency of the ScriptGenE Framework is tested in three fully automated experiments

Probabilistic spatial analysis in quantitative microscopy with uncertainty-aware cell detection using deep Bayesian regression

The investigation of biological systems with three-dimensional microscopy demands automatic cell identification methods that not only are accurate but also can imply the uncertainty in their predictions. The use of deep learning to regress density maps is a popular successful approach for extracting cell coordinates from local peaks in a postprocessing step, which then, however, hinders any meaningful probabilistic output. We propose a framework that can operate on large microscopy images and output probabilistic predictions (i) by integrating deep Bayesian learning for the regression of uncertainty-aware density maps, where peak detection algorithms generate cell proposals, and (ii) by learning a mapping from prediction proposals to a probabilistic space that accurately represents the chances of a successful prediction. Using these calibrated predictions, we propose a probabilistic spatial analysis with Monte Carlo sampling. We demonstrate this in a bone marrow dataset, where our proposed methods reveal spatial patterns that are otherwise undetectable