ETH Zurich

About: ETH Zurich is a education organization based out in Zurich, Switzerland. It is known for research contribution in the topics: Population & Computer science. The organization has 48393 authors who have published 122408 publications receiving 5111383 citations. The organization is also known as: Swiss Federal Institute of Technology in Zurich & Eidgenössische Technische Hochschule Zürich.

The LRU-K page replacement algorithm for database disk buffering

Abstract: This paper introduces a new approach to database disk buffering, called the LRU-K method The basic idea of LRU-K is to keep track of the times of the last K references to popular database pages, using this information to statistically estimate the interarrival times of references on a page by page basis Although the LRU-K approach performs optimal statistical inference under relatively standard assumptions, it is fairly simple and incurs little bookkeeping overhead As we demonstrate with simulation experiments, the LRU-K algorithm surpasses conventional buffering algorithms in discriminating between frequently and infrequently referenced pages In fact, LRU-K can approach the behavior of buffering algorithms in which page sets with known access frequencies are manually assigned to different buffer pools of specifically tuned sizes Unlike such customized buffering algorithms however, the LRU-K method is self-tuning, and does not rely on external hints about workload characteristics Furthermore, the LRU-K algorithm adapts in real time to changing patterns of access

Oxidic nanotubes and nanorods--anisotropic modules for a future nanotechnology.

Abstract: The discovery of carbon nanotubes in 1991 is a milestone in nanomaterials research. Since then, more and more anisotropic nanoparticles have been detected and characterized. The development of nanodevices might benefit from the distinct morphology and high aspect ratio of nanorods and nanotubes as these can be functionalized in unique ways such as incorporation of nanorods in nanotubes. Downscaling a broad range of materials to 1D nanoscopic structures is currently the focus of a rapidly growing scientific community. Developing general pathways to this goal would transfer a wide variety of properties to the nanoscale-a spectrum of phenomena so diverse that it would cover not only inorganic systems but all of materials science. Synthesis of real functional materials, however, always involves considerable synthetic ingenuity, interdisciplinary collaboration, as well as technological and economical realism. The major topic of this review is to provide a survey of recent progress in the synthesis of oxidic nanotubes and nanorods-with their non-oxidic counterparts briefly highlighted-and to outline the major synthetic routes leading to them. With the challenges of synthesizing bulk oxidic materials in mind, the establishment of trustworthy and uncomplicated ways of providing them as anisotropic nano-modules on an industrial scale appears to be more or less serendipity. Of the methods utilized in nanotube and nanorod synthesis solvothermal processes have emerged as powerful tools for generalizing and systematizing controlled syntheses of nano-morphologies. The flexibility and reliability of this synthetic approach is demonstrated here for the transformation of transition-metal oxides into high-quality anisotropic nanomaterials.

Symmetry and magnitude of spin-orbit torques in ferromagnetic heterostructures

Abstract: Spin–orbit torques in heavy metal/ferromagnetic layers have a complex dependence on the magnetization direction. This dependence can be exploited to increase the efficiency of spin–orbit torques.

Count-based differential expression analysis of RNA sequencing data using R and Bioconductor

Abstract: RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e.g., tissues, perturbations) while optionally adjusting for other systematic factors that affect the data-collection process. There are a number of subtle yet crucial aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a state-of-the-art computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and, in particular, on two widely used tools, DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10 samples) can be <1 h, with computation time <1 d using a standard desktop PC.

ClassCut for unsupervised class segmentation

Abstract: We propose a novel method for unsupervised class segmentation on a set of images. It alternates between segmenting object instances and learning a class model. The method is based on a segmentation energy defined over all images at the same time, which can be optimized efficiently by techniques used before in interactive segmentation. Over iterations, our method progressively learns a class model by integrating observations over all images. In addition to appearance, this model captures the location and shape of the class with respect to an automatically determined coordinate frame common across images. This frame allows us to build stronger shape and location models, similar to those used in object class detection. Our method is inspired by interactive segmentation methods [1], but it is fully automatic and learns models characteristic for the object class rather than specific to one particular object/image. We experimentally demonstrate on the Caltech4, Caltech101, and Weizmann horses datasets that our method (a) transfers class knowledge across images and this improves results compared to segmenting every image independently; (b) outperforms Grabcut [1] for the task of unsupervised segmentation; (c) offers competitive performance compared to the state-of-the-art in unsupervised segmentation and in particular it outperforms the topic model [2].