scholarly journals Recent Advances in Cybersecurity and Safety Architectures in Automotive, IT, and Connected Services

2021 ◽  
Vol 27 (8) ◽  
pp. 793-795
Author(s):  
Richard Messnarz ◽  
Ricardo Colomo-Palacios ◽  
Georg Macher ◽  
Andreas Riel ◽  
Miklos Biro
Keyword(s):  
State Of The Art ◽  
It Security ◽  
Functional Safety ◽  
Special Issue ◽  
High Quality ◽  
International Network ◽  
Automotive Safety ◽  
European Conference ◽  
Current State ◽  
Self Learning

This is a special issue in cooperation with EuroSPI (www.eurospi.net). EuroSPI represents a large international network of renowned experts and annual European conference series running successfully since its foundation in 1994. From 2013 onwards, an international functional safety and from 2016 onwards a functional safety and cybersecurity workshop has been established, to which leading European and Asian industry and research have been actively contributing to. High-quality,  original  papers  about  best  practices  for  implementing  functional  safety  and cybersecurity in automotive, IT, and connected services have been selected for this special issue. They provide insights into the current state of the art implementations in automotive safety and cybersecurity, IT security, and safety in future highly autonomous self-learning vehicles.

scholarly journals Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 99 ◽  
Author(s):  
Kleopatra Pirpinia ◽  
Peter A. N. Bosman ◽  
Jan-Jakob Sonke ◽  
Marcel van Herk ◽  
Tanja Alderliesten
Keyword(s):  
Machine Learning ◽  
Image Registration ◽  
State Of The Art ◽  
Deformable Image Registration ◽  
Optimization Approach ◽  
High Quality ◽  
Trade Off ◽  
Multi Objective ◽  
Current State ◽  
Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

scholarly journals Multifaceted origins of sex differences in the brain

2016 ◽  
Vol 371 (1688) ◽  
pp. 20150106 ◽  
Author(s):  
Margaret M. McCarthy
Keyword(s):  
Sex Differences ◽  
State Of The Art ◽  
Human Subjects ◽  
Special Issue ◽  
Molecular Analyses ◽  
The Past ◽  
Current State ◽  
Biological Variable ◽  
Males And Females ◽  
The Brain

Studies of sex differences in the brain range from reductionistic cell and molecular analyses in animal models to functional imaging in awake human subjects, with many other levels in between. Interpretations and conclusions about the importance of particular differences often vary with differing levels of analyses and can lead to discord and dissent. In the past two decades, the range of neurobiological, psychological and psychiatric endpoints found to differ between males and females has expanded beyond reproduction into every aspect of the healthy and diseased brain, and thereby demands our attention. A greater understanding of all aspects of neural functioning will only be achieved by incorporating sex as a biological variable. The goal of this review is to highlight the current state of the art of the discipline of sex differences research with an emphasis on the brain and to contextualize the articles appearing in the accompanying special issue.

scholarly journals Polymer Clay Nano-Composites

Polymers ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1445
Author(s):  
Stefano Leporatti
Keyword(s):  
Drug Delivery ◽  
Composite Materials ◽  
Polymer Composite ◽  
State Of The Art ◽  
Environmental Water ◽  
Water Remediation ◽  
Nano Composites ◽  
Special Issue ◽  
Current State ◽  
Exciting Area

Clay–polymer composite materials is an exciting area of research and this Special Issue aims to address the current state-of-the-art of “Polymer Clay Nano-Composites” for several applications, among them antibacterial, environmental, water remediation, dental, drug delivery and others [...]

Special Issue on Multiaxis Control and Multitasking Machine Tools

2007 ◽  
Vol 1 (2) ◽  
pp. 77-77 ◽  
Author(s):  
Yoshimi Takeuchi ◽  
Keyword(s):  
Machine Tools ◽  
State Of The Art ◽  
Numerical Control ◽  
Plant Production ◽  
Precision Machining ◽  
Accuracy Evaluation ◽  
Special Issue ◽  
Complex Products ◽  
Current State ◽  
Control Machine

Machine tools using numerical control (NC) devices are typical mechatronics products and a powerful way to automate plant production. The introduction of multiaxis control and multitasking machine tools to workshops is growing to meet the requirements of highly efficient, precision machining of a variety of complex products and mold dies. The increase in the number of control axes and multitasking capability in one chucking process enable machine tools to manufacture complex products efficiently and accurately. Given the strong attention and interest multiaxis control and multitasking machine tools are attracting, it is about time to introduce the current state of the art of these tools and their practical and applicable technologies, especially in Japan. This special issue covers the development of 5-axis control machining centers, machine tools having multispindle heads with 5-axis control, 5-axis control CAMs, accuracy evaluation for 5-axis control machine tools, and more. We thank the authors for their interesting papers to this special issue, and are certain that both general readers and specialists will find the information they provide both interesting and informative.

Thermoelectric Properties of C-Axis-Oriented Ca3Co4O9+δ Films Grown on MgO-Buffered Si (100) by PLD Technique

2010 ◽  
Vol 29-32 ◽  
pp. 1913-1918
Author(s):  
Xia Zhang ◽  
Hong Chen ◽  
Qiu Hui Liao ◽  
Xia Li
Keyword(s):  
Thin Films ◽  
State Of The Art ◽  
Pulsed Laser ◽  
Buffer Layers ◽  
Laser Deposition ◽  
X Ray Diffraction ◽  
High Quality ◽  
X Ray ◽  
Current State ◽  
Good Crystallinity

High-quality c-axis-oriented Ca3Co4O9+δ thin films have been grown directly on Si (100) wafers with inserting MgO buffer layers by pulsed-laser deposition (PLD). X-ray diffraction and scan electron microscopy show good crystallinity of the Ca3Co4O9+δ films. The resistivity and Seebeck coefficient of the Ca3Co4O9+δ thin films on Si (100) substrates are 9.8 mΩcm and 189 μV/K at the temperature of 500K, respectively, comparable to the single-crystal samples. This advance demonstrates the possibility of integrating the cobaltate-based high thermoelectric materials with the current state-of-the-art silicon technology for thermoelectricity-on-a-chip applications.

scholarly journals Digital Archiving in Archaeology: The State of the Art. Introduction

2021 ◽  
Author(s):  
Julian D. Richards ◽  
Ulf Jakobsson ◽  
David Novák ◽  
Benjamin Štular ◽  
Holly Wright
Keyword(s):  
State Of The Art ◽  
The State ◽  
History Of Archaeology ◽  
Special Issue ◽  
Digital Archiving ◽  
Current State ◽  
The World ◽  
History Of

The articles in this special issue demonstrate significant differences in digital archiving capacity in different countries. In part these reflect differences in the history of archaeology in each country, its relationship to the state, whether it is centralised or decentralised, state-led or commercially driven. They also reflect some of the different attitudes to archaeology across the world, most recently explored in a survey conducted under the auspices of the NEARCH project. They reflect a snapshot in time, but our aim is to record the current state-of-the-art in each country, to inform knowledge, stimulate discussion, and to provoke change.

scholarly journals Special Issue: Protein Simulations – Current State of the Art

2020 ◽  
Vol 60 (7) ◽  
pp. 654-654
Author(s):  
Ran Friedman ◽  
Yaakov Levy
Keyword(s):  
State Of The Art ◽  
Special Issue ◽  
Current State

scholarly journals Deep Convolutional Neural Network for Pedestrian Detection with Multi-Levels Features Fusion

2018 ◽  
Vol 232 ◽  
pp. 01061
Author(s):  
Danhua Li ◽  
Xiaofeng Di ◽  
Xuan Qu ◽  
Yunfei Zhao ◽  
Honggang Kong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Deep Convolutional Neural Network ◽  
High Quality ◽  
Low Level ◽  
Features Fusion ◽  
Current State ◽  
High Level

Pedestrian detection aims to localize and recognize every pedestrian instance in an image with a bounding box. The current state-of-the-art method is Faster RCNN, which is such a network that uses a region proposal network (RPN) to generate high quality region proposals, while Fast RCNN is used to classifiers extract features into corresponding categories. The contribution of this paper is integrated low-level features and high-level features into a Faster RCNN-based pedestrian detection framework, which efficiently increase the capacity of the feature. Through our experiments, we comprehensively evaluate our framework, on the Caltech pedestrian detection benchmark and our methods achieve state-of-the-art accuracy and present a competitive result on Caltech dataset.

Functions and Kinds of Art Works and Other Artifacts

2019 ◽  
Vol 96 (1) ◽  
pp. 1-18
Author(s):  
Amrei Bahr ◽  
Massimiliano Carrara ◽  
Ludger Jansen
Keyword(s):  
State Of The Art ◽  
Special Importance ◽  
Special Issue ◽  
General Philosophy ◽  
Philosophical Discourse ◽  
Current State ◽  
Art Works

Currently, there is not yet a full-fledged philosophical sub-discipline devoted to artifacts. In order to establish such a general philosophical discourse on artifacts, two topics are of special importance: artifact functionality and artifact categorization. Both are central to the question of what artifacts are in general and in particular. This introduction first presents the current state of the art in the debates on functions, both in general and in the domain of artifacts in particular. It then unfolds the three debates relevant for artifact kinds, namely the ontological, epistemological and semantic debates on artifact categorization, and presents the most important theory options currently under scrutiny in these fields. It proceeds by introducing the contributions in this special issue on the functions and kinds of art works and other artifacts, and discusses possible perspectives for a general philosophy of artifacts.

scholarly journals Frame and Feature-Context Video Super-Resolution

2019 ◽  
Vol 33 ◽  
pp. 5597-5604
Author(s):  
Bo Yan ◽  
Chuming Lin ◽  
Weimin Tan
Keyword(s):  
Information Flow ◽  
State Of The Art ◽  
Super Resolution ◽  
Sliding Window ◽  
Visual Quality ◽  
Local Network ◽  
Context Information ◽  
High Quality ◽  
Current State ◽  
Inter Frame

For video super-resolution, current state-of-the-art approaches either process multiple low-resolution (LR) frames to produce each output high-resolution (HR) frame separately in a sliding window fashion or recurrently exploit the previously estimated HR frames to super-resolve the following frame. The main weaknesses of these approaches are: 1) separately generating each output frame may obtain high-quality HR estimates while resulting in unsatisfactory flickering artifacts, and 2) combining previously generated HR frames can produce temporally consistent results in the case of short information flow, but it will cause significant jitter and jagged artifacts because the previous super-resolving errors are constantly accumulated to the subsequent frames.In this paper, we propose a fully end-to-end trainable frame and feature-context video super-resolution (FFCVSR) network that consists of two key sub-networks: local network and context network, where the first one explicitly utilizes a sequence of consecutive LR frames to generate local feature and local SR frame, and the other combines the outputs of local network and the previously estimated HR frames and features to super-resolve the subsequent frame. Our approach takes full advantage of the inter-frame information from multiple LR frames and the context information from previously predicted HR frames, producing temporally consistent highquality results while maintaining real-time speed by directly reusing previous features and frames. Extensive evaluations and comparisons demonstrate that our approach produces state-of-the-art results on a standard benchmark dataset, with advantages in terms of accuracy, efficiency, and visual quality over the existing approaches.

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