Ablation and functionalization of flexographic printing forms using femtosecond lasers for additively manufactured polymer-optical waveguides

An efficient and low-cost approach to manufacture Opto-Mechatronic Interconnect Devices will be obligatory to handle the strongly increasing amount of data. The presented approach is based on a flexographic printing process. To adjust the transferred material the printing form is functionalized by means of laser-induced structures using an ultrashort-pulsed laser. The long-term goal is to adjust the printing result through microstructures in the printing form in order to create spatially resolved material transfer. In this work, first the ablation parameters are investigated at different repetition rates using a femtosecond laser. Further, a line structure is inserted in the material transferring areas of the printing form, which is consequently widened. Its influence on the printing result is presented. © 2020 The Authors. Published by Elsevier B.V

Towards a Soft Evaluation and Refinement of Tagging in Digital Humanities

In this paper we estimate the soundness of tagging in digital repositories within the field of Digital Humanities by studying the (semantic) conceptual structure behind the folksnonomy. The use of association rules associated to this conceptual structure (Stem and Luxenburger basis) allows to faithfully (from a semantic point of view) complete the tagging (or suggest such a completion).Ministerio de Economía y Competitividad TIN2013-41086-PJunta de Andalucía TIC-606

Unsupervised machine learning on encrypted data

In the context of Fully Homomorphic Encryption, which allows computations on encrypted data, Machine Learning has been one of the most popular applications in the recent past. All of these works, however, have focused on supervised learning, where there is a labeled training set that is used to configure the model. In this work, we take the first step into the realm of unsupervised learning, which is an important area in Machine Learning and has many real-world applications, by addressing the clustering problem. To this end, we show how to implement the K-Means-Algorithm. This algorithm poses several challenges in the FHE context, including a division, which we tackle by using a natural encoding that allows division and may be of independent interest. While this theoretically solves the problem, performance in practice is not optimal, so we then propose some changes to the clustering algorithm to make it executable under more conventional encodings. We show that our new algorithm achieves a clustering accuracy comparable to the original K-Means-Algorithm, but has less than $5\%$ of its runtime

Synthesis by High-Efficiency Liquid-Phase (HELP) Method of Oligonucleotides Conjugated with High-Molecular Weight Polyethylene Glycols (PEGs)

The chemical modification of synthetic oligonucleotides has recently been investigated to improve their pharmacological utilization. In addition to chemical alterations of the backbone and of the heterocyclic bases, their conjugation with amphiphylic moieties, such as the polyethylene glycol has been proposed. The large scale production of these molecules as demanded for commercial purposes is hampered by the heterogeneity of the solid-phase processes and by the low reactivity of high-molecular weight PEGs in solution. A new synthetic procedure based on the recently developed liquid-phase method (HELP), has been set up to overcome these limitations

What Do Computer Scientists Tweet? Analyzing the Link-Sharing Practice on Twitter

Twitter communication has permeated every sphere of society. To highlight and share small pieces of information with possibly vast audiences or small circles of the interested has some value in almost any aspect of social life. But what is the value exactly for a scientific field? We perform a comprehensive study of computer scientists using Twitter and their tweeting behavior concerning the sharing of web links. Discerning the domains, hosts and individual web pages being tweeted and the differences between computer scientists and a Twitter sample enables us to look in depth at the Twitter-based information sharing practices of a scientific community. Additionally, we aim at providing a deeper understanding of the role and impact of altmetrics in computer science and give a glance at the publications mentioned on Twitter that are most relevant for the computer science community. Our results show a link sharing culture that concentrates more heavily on public and professional quality information than the Twitter sample does. The results also show a broad variety in linked sources and especially in linked publications with some publications clearly related to community-specific interests of computer scientists, while others with a strong relation to attention mechanisms in social media. This refers to the observation that Twitter is a hybrid form of social media between an information service and a social network service. Overall the computer scientists’ style of usage seems to be more on the information-oriented side and to some degree also on professional usage. Therefore, altmetrics are of considerable use in analyzing computer science

"CAN Stop" - Implementation and evaluation of a secondary group prevention for adolescent and young adult cannabis users in various contexts - study protocol

<p>Abstract</p> <p>Background</p> <p>Current research shows that overall numbers for cannabis use among adolescents and young adults dropped in recent years. However, this trend is much less pronounced in continuous cannabis use. With regard to the heightened risk for detrimental health- and development-related outcomes, adolescents and young adults with continuous cannabis use need special attention. The health services structure for adolescents and young adults with substance related problems in Germany, is multifaceted, because different communal, medical and judicial agencies are involved. This results in a rather decentralized organizational structure of the help system. This and further system-inherent characteristics make the threshold for young cannabis users rather high. Because of this, there is a need to establish evidence-based low-threshold help options for young cannabis users, which can be easily disseminated. Therefore, a training programme for young cannabis users (age 14-21) was developed in the "CAN Stop" project. Within the project, we seek to implement and evaluate the training programme within different institutions of the help system. The evaluation is sensitive to the different help systems and their specific prerequisites. Moreover, within this study, we also test the practicability of a training provision through laypersons.</p> <p>Methods/Design</p> <p>The CAN Stop study is a four-armed randomized wait-list controlled trial. The four arms are needed for the different help system settings, in which the CAN Stop training programme is evaluated: (a) the drug addiction aid and youth welfare system, (b) the out-patient medical system, (c) the in-patient medical system and (d) prisons for juvenile offenders. Data are collected at three points, before and after the training or a treatment as usual, and six months after the end of either intervention.</p> <p>Discussion</p> <p>The CAN Stop study is expected to provide an evidence-based programme for young cannabis users seeking to reduce or quit their cannabis use. Moreover, we seek to gain knowledge about the programme's utility within different settings of the German help system for young cannabis users and information about the settings' specific clientele. The study protocol is discussed with regard to potential difficulties within the different settings.</p> <p>Trial registration</p> <p>ISRCTN: <a href="http://www.controlled-trials.com/ISRCTN57036983">ISRCTN57036983</a></p

Social navigation

In this chapter we present one of the pioneer approaches in supporting users in navigating the complex information spaces, social navigation support. Social navigation support is inspired by natural tendencies of individuals to follow traces of each other in exploring the world, especially when dealing with uncertainties. In this chapter, we cover details on various approaches in implementing social navigation support in the information space as we also connect the concept to supporting theories. The first part of this chapter reviews related theories and introduces the design space of social navigation support through a series of example applications. The second part of the chapter discusses the common challenges in design and implementation of social navigation support, demonstrates how these challenges have been addressed, and reviews more recent direction of social navigation support. Furthermore, as social navigation support has been an inspirational approach to various other social information access approaches we discuss how social navigation support can be integrated with those approaches. We conclude with a review of evaluation methods for social navigation support and remarks about its current state

Towards a Practical Cluster Analysis over Encrypted Data

Cluster analysis is one of the most significant unsupervised machine learning tasks, and it is utilized in various fields associated with privacy issues including bioinformatics, finance and image processing. In this paper, we propose a practical solution for privacy-preserving cluster analysis based on homomorphic encryption~(HE). Our work is the first HE solution for the mean-shift clustering algorithm. To reduce the super-linear complexity of the original mean-shift algorithm, we adopt a novel random sampling method called dust sampling which perfectly fits in HE and achieves the linear complexity. We also substitute non-polynomial kernels by a new polynomial kernel so that it can be efficiently computed in HE. The HE implementation of our modified mean-shift clustering algorithm based on the approximate HE scheme HEAAN shows prominent performance in terms of speed and accuracy. It takes about $30$ minutes with $99\%$ accuracy over several public datasets with hundreds of data, and even for the dataset with $262,144$ data it takes only $82$ minutes applying SIMD operations in HEAAN. Our results outperform the previously best known result (SAC 2018) over $400$ times

Design of passive cooling by night ventilation: evaluation of a parametric model and building simulation with measurements

At the new institute building of Fraunhofer ISE, both mechanical and free night ventilation is used for passive cooling of the offices. The results from a long-term monitoring show, that room temperatures are comfortable even at high ambient air temperatures. In two offices, experiments were carried out in order to determine the efficiency of night ventilation dependent on air change rate, solar and internal heat gains. The aim is to identify characteristic building parameters and to determine the night ventilation effect with these parameters. The experiments (one room with and one without night ventilation) are evaluated by using both a parametric model and the ESP-r building simulation programme. Both models are merged in order to develop a method for data evaluation in office buildings with night ventilation and to provide a simple model for integration in a building management system