German and Israeli Innovation: The Best of Two Worlds

This study reviews – through desk research and expert interviews with Mittelstand companies, startups and ecosystem experts – the current status of the Israeli startup ecosystem and the Mittelstand region of North Rhine- Westphalia (NRW), Germany. As a case study, it highlights potential opportunities for collaboration and analyzes different engagement modes that might serve to connect the two regions. The potential synergies between the two economies are based on a high degree of complementarity. A comparison of NRW’s key verticals and Israel’s primary areas of innovation indicates that there is significant overlap in verticals, such as artificial intelligence (AI), the internet of things (IoT), sensors and cybersecurity. Israeli startups can offer speed, agility and new ideas, while German Mittelstand companies can contribute expertise in production and scaling, access to markets, capital and support. The differences between Mittelstand companies and startups are less pronounced than those between startups and big corporations. However, three current barriers to fruitful collaboration have been identified: 1) a lack of access, 2) a lack of transparency regarding relevant players in the market, and 3) a lack of the internal resources needed to select the right partners, often due to time constraints or a lack of internal expertise on this issue. To ensure that positive business opportunities ensue, Mittelstand companies and startups alike have to be proactive in their search for cooperation partners and draw on a range of existing engagement modes (e.g., events, communities, accelerators). The interviews and the research conducted for this study made clear that no single mode of engagement can address all the needs and challenges associated with German-Israeli collaboration

New Constraints on Supersymmetry Using Neutrino Telescopes

We demonstrate that megaton-mass neutrino telescopes are able to observe the signal from long-lived particles beyond the Standard Model, in particular the stau, the supersymmetric partner of the tau lepton. Its signature is an excess of charged particle tracks with horizontal arrival directions and energy deposits between 0.1 and 1 TeV inside the detector. We exploit this previously-overlooked signature to search for stau particles in the publicly available IceCube data. The data shows no evidence of physics beyond the Standard Model. We derive a new lower limit on the stau mass of $320$ GeV (95\% C.L.) and estimate that this new approach, when applied to the full data set available to the IceCube collaboration, will reach world-leading sensitivity to the stau mass ($m_{\tilde{\tau}}=450\,\mathrm{GeV}$)

Automatic discovery of cross-family sequence features associated with protein function

BACKGROUND: Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterised protein families and in comparative genomics. Until now, this problem has been approached using machine learning techniques that attempt to predict membership, or otherwise, to predefined functional categories or subcellular locations. A potential drawback of this approach is that the human-designated functional classes may not accurately reflect the underlying biology, and consequently important sequence-to-function relationships may be missed. RESULTS: We show that a self-supervised data mining approach is able to find relationships between sequence features and functional annotations. No preconceived ideas about functional categories are required, and the training data is simply a set of protein sequences and their UniProt/Swiss-Prot annotations. The main technical aspect of the approach is the co-evolution of amino acid-based regular expressions and keyword-based logical expressions with genetic programming. Our experiments on a strictly non-redundant set of eukaryotic proteins reveal that the strongest and most easily detected sequence-to-function relationships are concerned with targeting to various cellular compartments, which is an area already well studied both experimentally and computationally. Of more interest are a number of broad functional roles which can also be correlated with sequence features. These include inhibition, biosynthesis, transcription and defence against bacteria. Despite substantial overlaps between these functions and their corresponding cellular compartments, we find clear differences in the sequence motifs used to predict some of these functions. For example, the presence of polyglutamine repeats appears to be linked more strongly to the "transcription" function than to the general "nuclear" function/location. CONCLUSION: We have developed a novel and useful approach for knowledge discovery in annotated sequence data. The technique is able to identify functionally important sequence features and does not require expert knowledge. By viewing protein function from a sequence perspective, the approach is also suitable for discovering unexpected links between biological processes, such as the recently discovered role of ubiquitination in transcription

Multi-scale spatio-temporal analysis of human mobility

The recent availability of digital traces generated by phone calls and online logins has significantly increased the scientific understanding of human mobility. Until now, however, limited data resolution and coverage have hindered a coherent description of human displacements across different spatial and temporal scales. Here, we characterise mobility behaviour across several orders of magnitude by analysing ∼850 individuals' digital traces sampled every ∼16 seconds for 25 months with ∼10 meters spatial resolution. We show that the distributions of distances and waiting times between consecutive locations are best described by log-normal and gamma distributions, respectively, and that natural time-scales emerge from the regularity of human mobility. We point out that log-normal distributions also characterise the patterns of discovery of new places, implying that they are not a simple consequence of the routine of modern life

Magnetic materials used in electrical machines : a comparison and selection guide for early machine design

This article presents an up-to-date magnetic material investigation and overview on soft magnetic materials used in rotating electrical machines. The focus is on small-to-medium-sized high-performance and high-efficiency permanent-magnet and induction motors for different application scenarios. The investigated materials include fully processed silicon-iron (SiFe), nickel-iron (NiFe), and cobalt-iron (CoFe) lamination steels as well as soft magnetic composites (SMCs) and amorphous magnetic materials. This article focuses on the magnetic properties and iron losses as well as the manufacturing influence and required thermal treatments during the manufacturing process. A new loss-to-flux-density factor is introduced to compare the magnetization curve and the iron losses of different materials within the same diagram. This article provides a review and comparison of magnetic substances for use in high-performance machines