We can only solve the problem with coordinated action:Two-factor authentification is a must

Digitisation is turning large companies into gold mines for cybercriminals. How can the public and private sectors arm themselves to combat this cyber threat

Modular Micro Propulsion System

The miniaturization of space applicable devices by means of MEMS technology is pursued by many research groups. MEMS devices are often designed as stand alone and require individual packaging which often makes them still quite large. Focusing on the integration of several MEMS components has the advantage of reducing size and mass much more. An integrated and miniaturized cold gas propulsion system for micro satellites is presented which consists of a valve, a particle filter, a pressure sensor, a nozzle and a gas tank. By selecting a convenient package first and adjusting the MEMS part to fit the package, costs are reduced and modularity is obtained. The baseline of the system is a glass tube bonded on a silicon disc which contains a valve seat as shown in Figure 1. The valve is normally closed by an embossed membrane which is stacked inside the glass tube. A piezo-disc is glued to the boss of the membrane to actuate the valve. The glass tube is functioning as hermetically sealed package as well as fluidic interconnect with the macro world. The pressure sensor and particle filter are suspended in the glass tube. This integrated system is connected to a pressurized N2 gas tank which is developed by TNO [1]. The tank contains 8 cold gas generators which makes it possible to reduce the working pressure to 3.4bar withoutcompromising on the amount of gas. During the symposium the technical development and results will be presented

Design of a cold gas micro thruster

Keywords: Micro satellites, Micro propulsion, MEMS technologie

Als algoritmen routinewerk overnemen nekt dat ook expertise

Kunstmatige intelligentie gaat ons werk transformeren. Ook juristen ontkomen er niet aan. Als je tot voor kort het maken van een contract wilde automatiseren, moest je alle opties en keuzes vooraf bedenken en in het model stoppen. Je kreeg dus als resultaat wat je erin stopte, niets meer, niets minder. Nu voer je een zelflerend algoritme een grote stapel contracten en vindt het zelf zijn weg in alle relevante opties en bijbehorende bepalingen die ervaren advocaten door de jaren hebben bedacht. Het algoritme als neerslag van onze collectief opgebouwde contextuele ervaring in het opstellen van contracten, als dat maar geen banen gaat kosten

An empirically driven guide on using Bayes factors for M/EEG decoding

Bayes factors can be used to provide quantifiable evidence for contrasting hypotheses and have thus become increasingly popular in cognitive science. However, Bayes factors are rarely used to statistically assess the results of neuroimaging experiments. Here, we provide an empirically driven guide on implementing Bayes factors for time-series neural decoding results. Using real and simulated magnetoencephalography (MEG) data, we examine how parameters such as the shape of the prior and data size affect Bayes factors. Additionally, we discuss the benefits Bayes factors bring to analysing multivariate pattern analysis data and show how using Bayes factors can be used instead or in addition to traditional frequentist approaches

Effects of Synaptic and Myelin Plasticity on Learning in a Network of Kuramoto Phase Oscillators

Models of learning typically focus on synaptic plasticity. However, learning is the result of both synaptic and myelin plasticity. Specifically, synaptic changes often co-occur and interact with myelin changes, leading to complex dynamic interactions between these processes. Here, we investigate the implications of these interactions for the coupling behavior of a system of Kuramoto oscillators. To that end, we construct a fully connected, one-dimensional ring network of phase oscillators whose coupling strength (reflecting synaptic strength) as well as conduction velocity (reflecting myelination) are each regulated by a Hebbian learning rule. We evaluate the behavior of the system in terms of structural (pairwise connection strength and conduction velocity) and functional connectivity (local and global synchronization behavior). We find that for conditions in which a system limited to synaptic plasticity develops two distinct clusters both structurally and functionally, additional adaptive myelination allows for functional communication across these structural clusters. Hence, dynamic conduction velocity permits the functional integration of structurally segregated clusters. Our results confirm that network states following learning may be different when myelin plasticity is considered in addition to synaptic plasticity, pointing towards the relevance of integrating both factors in computational models of learning.Comment: 39 pages, 15 figures This work is submitted in Chaos: An Interdisciplinary Journal of Nonlinear Scienc