Few-node subgraphs are the smallest collective units in a network that can be investigated. They are beyond the scale of individual nodes but more local than, for example, communities. When statistically over-or under-represented, they are called network motifs. Network motifs have been interpreted as building blocks that shape the dynamic behaviour of networks. It is this promise of potentially explaining emergent properties of complex systems with relatively simple structures that led to an interest in network motifs in an ever-growing number of studies and across disciplines. Here, we discuss artefacts in the analysis of network motifs arising from discrepancies between the network under investigation and the pool of random graphs serving as a null model. Our aim was to provide a clear and accessible catalogue of such incongruities and their effect on the motif signature. As a case study, we explore the metabolic network of Escherichia coli and show that only by excluding ever more artefacts from the motif signature a strong and plausible correlation with the essentiality profile of metabolic reactions emerges.

Abstractj There has been a lot of research in recreational uses of robots. A robot drawing the portrait of a human face is one such famous task. This makes the robot behavior more human-like and entertaining. There have been several demonstrations of portrait drawing robots in past few years. But the existing techniques can draw only on pre-calibrated and flat surfaces. This paper demonstrates a robot equipped with force sensing capability that can draw portraits on a non-calibrated, arbitrarily shaped surface. The robot is able to draw on a noncalibrated surface by orienting its drawing pen normal to the drawing surface, the penj s-orientation being computed from the forces being sensed. In this way, the robot is also able to draw portraits on arbitrarily shaped surfaces without knowing the surface geometry. This avoids the need for calibration of robot with respect to the drawing surface. A number of portraits were drawn successfully on a flat surface without calibration. Also a map outline was drawn on a spherical globe to demonstrate the ability of robot to draw on an arbitrarily shaped surface.

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We demonstrate an application of finding target persons on a surveillance video. Each visually detected participant is tagged with a smartphone ID and the target person with the query ID is highlighted. This work is motivated by the fact that establishing associations between subjects observed in camera images and messages transmitted from their wireless devices can enable fast and reliable tagging. This is particularly helpful when target pedestrians need to be found on public surveillance footage, without the reliance on facial recognition. The underlying system uses a multimodal approach that leverages WiFi Fine Timing Measurements (FTM) and inertial sensor (IMU) data to associate each visually detected individual with a corresponding smartphone identifier. These smartphone measurements are combined strategically with RGB-D information from the camera, to learn affinity matrices using a multi-modal deep learning network. CCS CONCEPTS• Computing methodologies → Neural networks; • Humancentered computing → Ubiquitous and mobile computing systems and tools.