2009, Krstić, M., Fan, X., Grass, E., Gürkaynak, F.K.

In this paper we introduce a novel burst-mode GALS technique. The goal of this technique is improving the performance of the GALS approach for systems with predominantly bursty data transfer. This new technique has been used to implement a GALS-based version of a hardware accelerator of a 60 GHz OFDM baseband processor. The simulation results show a significant performance improvement in comparison with a classical implementation of GALS using pausible clocking. © 2009 Elsevier B.V. All rights reserved.

2021, Lüken, Arne, Bruckhaus, Maike, Kosfeld, Udo, Emondts, Meike, Wessling, Matthias

Tangential flow filtration (TFF) is a chemical unit operation used to purify and concentrate liquid suspensions of colloids, proteins, or cells. The solution flows tangentially across a membrane, such that a selective part of the fluid permeates the membrane while the filtrated matter is retained, increasing its concentration. TFF is a mild mechanical purification method that does not interact chemically with the filtrate. It is applied in sensitive separation tasks in protein chemistry, microbiology, or immunology. It is a fast alternative for dialysis applications, also applicable in the field of colloid purification. However, the costs of automated lab-scale devices (30,000 €) and the consumable membrane modules (100–600 €) make TFF currently hardly accessible for lab-scale polymer researchers. Therefore, we built a low-cost TFF system (2400 €) partly automated by an Arduino microcontroller and optimized for diafiltration buffer exchange and concentration processes in soft matter colloid research. We use medical hemodialysis membrane modules that only cost a share (20–50 €) of alternative TFF modules, and we demonstrate the functionality of the system for an exemplary colloidal microgel purification process.

2021, Dvurechensky, Pavel, Shtern, Shimrit, Staudigl, Mathias

First-order methods for solving convex optimization problems have been at the forefront of mathematical optimization in the last 20 years. The rapid development of this important class of algorithms is motivated by the success stories reported in various applications, including most importantly machine learning, signal processing, imaging and control theory. First-order methods have the potential to provide low accuracy solutions at low computational complexity which makes them an attractive set of tools in large-scale optimization problems. In this survey, we cover a number of key developments in gradient-based optimization methods. This includes non-Euclidean extensions of the classical proximal gradient method, and its accelerated versions. Additionally we survey recent developments within the class of projection-free methods, and proximal versions of primal-dual schemes. We give complete proofs for various key results, and highlight the unifying aspects of several optimization algorithms.

2020, Lezhnina, Olga, Kismihók, Gábor

In order to develop valid and reliable instruments, psychometric validation should be conducted as an iterative process that “requires a multi-method assessment” (Schimmack, 2019, p. 4). In this study, a multi-method psychometric approach was applied to a recently developed and validated scale, the Affinity for Technology Interaction (ATI) scale (Franke, Attig, & Wessel, 2018). The dataset (N ​= ​240) shared by the authors of the scale (Franke et al., 2018) was used. Construct validity of the ATI was explored by means of hierarchical clustering on variables, and its psychometric properties were analysed in accordance with an extended psychometric protocol (Dima, 2018) by methods of Classical Test Theory (CTT) and Item Response Theory (IRT). The results showed that the ATI is a unidimensional scale (homogeneity H ​= ​0.55) with excellent reliability (ω ​= ​0.90 [0.88-0.92]) and construct validity. Suggestions for further improvement of the ATI scale and the psychometric protocol were made.

2014, Blümel, Ina, Dietze, Stefan, Heller, Lambert, Jäschke, Robert, Mehlberg, Martin

Research information, i.e., data about research projects, organisations, researchers or research outputs such as publications or patents, is spread across the web, usually residing in institutional and personal web pages or in semi-open databases and information systems. While there exists a wealth of unstructured information, structured data is limited and often exposed following proprietary or less-established schemas and interfaces. Therefore, a holistic and consistent view on research information across organisational and national boundaries is not feasible. On the other hand, web crawling and information extraction techniques have matured throughout the last decade, allowing for automated approaches of harvesting, extracting and consolidating research information into a more coherent knowledge graph. In this work, we give an overview of the current state of the art in research information sharing on the web and present initial ideas towards a more holistic approach for boot-strapping research information from available web sources.

2011, de Angelis, M., Angonin, M.C., Beaufils, Q., Becker, Ch., Bertoldi, A., Bongs, K., Bourdel, T., Bouyer, P., Boyer, V., Dörscher, S., Duncker, H., Ertmer, W., Fernholz, T., Fromhold, T.M., Herr, W., Krüger, P., Kürbis, Ch., Mellor, C.J., Pereira Dos Santos, F., Peters, A., Poli, N., Popp, M., Prevedelli, M., Rasel, E.M., Rudolph, J., Schreck, F., Sengstock, K., Sorrentino, F., Stellmer, S., Tino, G.M., Valenzuela, T., Wendrich, T.J., Wicht, A., Windpassinger, P., Wolf, P.

We present iSense, a recently initiated FET project aiming to use Information and Communication Technologies (ICT) to develop a platform for portable quantum sensors based on cold atoms. A prototype of backpack-size highprecision force sensor will be built to demonstrate the concept.