NSSDC Conference on Mass Storage Systems and Technologies for Space and Earth Science Applications, volume 1

Papers and viewgraphs from the conference are presented. This conference served as a broad forum for the discussion of a number of important issues in the field of mass storage systems. Topics include magnetic disk and tape technologies, optical disks and tape, software storage and file management systems, and experiences with the use of a large, distributed storage system. The technical presentations describe, among other things, integrated mass storage systems that are expected to be available commercially. Also included is a series of presentations from Federal Government organizations and research institutions covering their mass storage requirements for the 1990's

NSSDC Conference on Mass Storage Systems and Technologies for Space and Earth Science Applications, volume 2

This report contains copies of nearly all of the technical papers and viewgraphs presented at the NSSDC Conference on Mass Storage Systems and Technologies for Space and Earth Science Application. This conference served as a broad forum for the discussion of a number of important issues in the field of mass storage systems. Topics include the following: magnetic disk and tape technologies; optical disk and tape; software storage and file management systems; and experiences with the use of a large, distributed storage system. The technical presentations describe, among other things, integrated mass storage systems that are expected to be available commercially. Also included is a series of presentations from Federal Government organizations and research institutions covering their mass storage requirements for the 1990's

A variable metric proximal stochastic gradient method: An application to classification problems

Due to the continued success of machine learning and deep learning in particular, supervised classification problems are ubiquitous in numerous scientific fields. Training these models typically involves the minimization of the empirical risk over large data sets along with a possibly non-differentiable regularization. In this paper, we introduce a stochastic gradient method for the considered classification problem. To control the variance of the objective's gradients, we use an automatic sample size selection along with a variable metric to precondition the stochastic gradient directions. Further, we utilize a non -monotone line search to automatize step size selection. Convergence results are provided for both convex and non-convex objective functions. Extensive numerical experiments verify that the suggested approach performs on par with stateof-the-art methods for training both statistical models for binary classification and artificial neural networks for multi-class image classification. The code is publicly available at https:// github .com /koblererich /lisavm

Implanto-Prosthetic Rehabilitation of the Mandible by Means of Two Implants

Edentulousness is a considerable problem in Croatia. So far prevention has not become the most important part of the dental profession. On the other hand,poor medical knowledge, reduced rights concerning health insurance costs as well as an increasing number of impoverished people in Croatia has resulted in postponed prosthetic rehabilitation. For the above mentioned reasons the Croatian people suffer from premature loss of their teeth. Also lower jaw atrophy occurs, which makes prosthetic rehabilitation even more difficult to achieve. In spite of some disadvantages, the double-implant borne prosthetic suprastructure has proved to be a simple and good solution to the patient\u27s problem, mainly because it is cost-effective. This particularly applies to Croatia patients. Over the last five years we have placed double -implants in 26 patients, in the anterior region of the mandible. The implants were placed in the region of the lower canine or slightly more mesially. Severe atrophy was determined in 13 patients (50%) which impeded their complete denture wearing even before the implant placement started. However, we made up for the loss in two patients by placing the implants again. This time we placed them slightly more mesially. We made one borne implant complete denture for one patient because the examination revealed severe atrophy in one segment of his mandible. In addition since the osseointegration prognosis for this patient was questionable we decided against any additional surgical treatment. Since the belts of the attached gingiva in our patients were wide enough and the diameters of the implants were not very long, no vestibuloplasty was necessary. We installed ITI, IMZ, ASTRA and Ankylos implants. All systems proved to be equally functional

Optimized Tuning of an EKF for State and Parameter Estimation in a Semitrailer

The Extended Kalman Filter (EKF) is a well-known method for state and parameter estimation in vehicle dynamics. However, for tuning the EKF, knowledge about the process and measurement noise is needed, which is usually unknown. Tuning the noise parameters manually is very time consuming, especially for systems with many states. Automated optimization based on the filtering errors promises less application time and better estimation performance, but also requires computing resources. This work presents two approaches for estimating the noise parameters of an EKF: A particle swarm optimization (PSO) and a gradient-based optimization. The EKF is applied to a nonlinear vehicle model of a tractor-semitrailer for estimating the steering and articulation angle as well as lateral and vertical tire forces based on real measurement data with different trailer loadings. Both methods are compared to each other to achieve the best estimation performance

Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods

MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware

Neurophysiological studies are typically conducted in laboratories with limited ecological validity, scalability, and generalizability of findings. This is a significant challenge for the development of brain-computer interfaces (BCIs), which ultimately need to function in unsupervised settings on consumer-grade hardware. We introduce MYND: A framework that couples consumer-grade recording hardware with an easy-to-use application for the unsupervised evaluation of BCI control strategies. Subjects are guided through experiment selection, hardware fitting, recording, and data upload in order to self-administer multi-day studies that include neurophysiological recordings and questionnaires. As a use case, we evaluate two BCI control strategies ("Positive memories" and "Music imagery") in a realistic scenario by combining MYND with a four-channel electroencephalogram (EEG). Thirty subjects recorded 70.4 hours of EEG data with the system at home. The median headset fitting time was 25.9 seconds, and a median signal quality of 90.2% was retained during recordings.Neural activity in both control strategies could be decoded with an average offline accuracy of 68.5% and 64.0% across all days. The repeated unsupervised execution of the same strategy affected performance, which could be tackled by implementing feedback to let subjects switch between strategies or devise new strategies with the platform.Comment: 9 pages, 5 figures. Submitted to PNAS. Minor revisio

Well-quasi-ordering versus clique-width : new results on bigenic classes.

Daligault, Rao and Thomassé conjectured that if a hereditary class of graphs is well-quasi-ordered by the induced subgraph relation then it has bounded clique-width. Lozin, Razgon and Zamaraev recently showed that this conjecture is not true for infinitely defined classes. For finitely defined classes the conjecture is still open. It is known to hold for classes of graphs defined by a single forbidden induced subgraph H, as such graphs are well-quasi-ordered and are of bounded clique-width if and only if H is an induced subgraph of P4P4. For bigenic classes of graphs i.e. ones defined by two forbidden induced subgraphs there are several open cases in both classifications. We reduce the number of open cases for well-quasi-orderability of such classes from 12 to 9. Our results agree with the conjecture and imply that there are only two remaining cases to verify for bigenic classes