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Surrogate Safety Measures (SSMs) are used to express road safety in terms of the safety risk in traffic conflicts. Typically, SSMs rely on assumptions regarding the future evolution of traffic participant trajectories to generate a measure of risk, restricting their applicability to scenarios where these assumptions are valid. In response to this limitation, we present the novel Probabilistic RISk Measure derivAtion (PRISMA) method. The objective of the PRISMA method is to derive SSMs that can be used to calculate in real time the probability of a specific event (e.g., a crash). The PRISMA method adopts a data-driven approach to predict the possible future traffic participant trajectories, thereby reducing the reliance on specific assumptions regarding these trajectories. Since the PRISMA is not bound to specific assumptions, the PRISMA method offers the ability to derive multiple SSMs for various scenarios. The occurrence probability of the specified event is based on simulations and combined with a regression model, this enables our derived SSMs to make real-time risk estimations. To illustrate the PRISMA method, an SSM is derived for risk evaluation during longitudinal traffic interactions. Since there is no known method to objectively estimate risk from first principles, i.e., there is no known risk ground truth, it is very difficult, if not impossible, to objectively compare the relative merits of two SSMs. Instead, we provide a method for benchmarking our derived SSM with respect to expected risk trends. The application of the benchmarking illustrates that the SSM matches the expected risk trends. Whereas the derived SSM shows the potential of the PRISMA method, future work involves applying the approach for other types of traffic conflicts, such as lateral traffic conflicts or interactions with vulnerable road users., Comment: Accepted for publication in Accident Analysis & Prevention. 28 pages, 5 figures, 1 table

Second Harmonic Generation Microscopy (SHG) is generally acknowledged as a powerful tool for the label-free 3D visualization of tissues and advanced materials, with one of its most popular applications being collagen imaging. Although the great need, progress in super-resolved SHG imaging lags behind the developments reported over the past years in fluorescence-based optical nanoscopy. In this work, we quantitatively show on collagenous tissues that by combining SHG imaging with re-scan microscopy resolutions that surpass the diffraction limit with ~1.4x become available. Besides Re-scan Second Harmonic Generation Microscopy (rSHG), we demonstrate as well super-resolved Re-scan Two-Photon Excited Fluorescence Microscopy (rTPEF). These two techniques are implemented by modifying a Re-scan Confocal Microscope (RCM), retaining its initial function, resulting thus in a multimodal rSHG/rTPEF/RCM system. Given the simplicity and flexibility of re-scan microscopy, we consider that the reported results are likely to augment the number and nature of applications relying on super-resolved non-linear optical imaging.

Semiconductor-superconductor hybrids are commonly used in research on topological quantum computation. Traditionally, top-down approaches involving dry or wet etching are used to define the device geometry. These often aggressive processes risk causing damage to material surfaces, giving rise to scattering sites particularly problematic for quantum applications. Here, we propose a method that maintains the flexibility and scalability of selective area grown nanowire networks while omitting the necessity of etching to create hybrid segments. Instead, it takes advantage of directional growth methods and uses bottom-up grown InP structures as shadowing objects to obtain selective metal deposition. The ability to lithographically define the position and area of these objects, and to grow a predefined height, ensures precise control of the shadowed region. We demonstrate the approach by growing InSb nanowire networks with well-defined Al and Pb islands. Cross-section cuts of the nanowires reveal a sharp, oxide-free interface between semiconductor and superconductor. By growing InP structures on both sides of in-plane nanowires, a combination of Pt and Pb can independently be shadow deposited, enabling a scalable and reproducible in-situ device fabrication. The semiconductor-superconductor nanostructures resulting from this approach are at the forefront of material development for Majorana based experiments.

Scenario-based methods for the assessment of Automated Vehicles (AVs) are widely supported by many players in the automotive field. Scenarios captured from real-world data can be used to define the scenarios for the assessment and to estimate their relevance. Therefore, different techniques are proposed for capturing scenarios from real-world data. In this paper, we propose a new method to capture scenarios from real-world data using a two-step approach. The first step consists in automatically labeling the data with tags. Second, we mine the scenarios, represented by a combination of tags, based on the labeled tags. One of the benefits of our approach is that the tags can be used to identify characteristics of a scenario that are shared among different type of scenarios. In this way, these characteristics need to be identified only once. Furthermore, the method is not specific for one type of scenario and, therefore, it can be applied to a large variety of scenarios. We provide two examples to illustrate the method. This paper is concluded with some promising future possibilities for our approach, such as automatic generation of scenarios for the assessment of automated vehicles., Comment: 8 pages, 8 figures, 4 tables

Promise Constraint Satisfaction Problems (PCSP) were proposed recently by Brakensiek and Guruswami arXiv:1704.01937 as a framework to study approximations for Constraint Satisfaction Problems (CSP). Informally a PCSP asks to distinguish between whether a given instance of a CSP has a solution or not even a specified relaxation can be satisfied. All currently known tractable PCSPs can be reduced in a natural way to tractable CSPs. Barto arXiv:1909.04878 presented an example of a PCSP over Boolean structures for which this reduction requires solving a CSP over an infinite structure. We give a first example of a PCSP over Boolean structures which reduces to a tractable CSP over a structure of size $3$ but not smaller. Further we investigate properties of PCSPs that reduce to systems of linear equations or to CSPs over structures with semilattice or majority polymorphism.

We introduce IP over Xylophone Players (IPoXP), a novel Internet protocol between two computers using xylophone-based Arduino interfaces. In our implementation, human operators are situated within the lowest layer of the network, transmitting data between computers by striking designated keys. We discuss how IPoXP inverts the traditional mode of human-computer interaction, with a computer using the human as an interface to communicate with another computer.

We consider unsupervised domain adaptation: given labelled examples from a source domain and unlabelled examples from a related target domain, the goal is to infer the labels of target examples. Under the assumption that features from pre-trained deep neural networks are transferable across related domains, domain adaptation reduces to aligning source and target domain at class prediction uncertainty level. We tackle this problem by introducing a method based on adversarial learning which forces the label uncertainty predictions on the target domain to be indistinguishable from those on the source domain. Pre-trained deep neural networks are used to generate deep features having high transferability across related domains. We perform an extensive experimental analysis of the proposed method over a wide set of publicly available pre-trained deep neural networks. Results of our experiments on domain adaptation tasks for image classification show that class prediction uncertainty alignment with features extracted from pre-trained deep neural networks provides an efficient, robust and effective method for domain adaptation., Comment: To appear in ICPRAM 2019

Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems.

The light flicker problem of high intensity discharge lamps is studied numerically and experimentally. It is shown that in some respects the systems behaves very similar to the forced Duffing oscillator with a softening spring. In particular, the jump phenomenon and hysteresis are observed in the simulations and in the experiments.

Operating high-intensity discharge lamps in the high frequency range (20-300 kHz) provides energy-saving and cost reduction potentials. However, commercially available lamp drivers do not make use of this operating strategy because light intensity fluctuations and even lamp destruction are possible. The reason for the fluctuating discharge arc are acoustic resonances in this frequency range that are excited in the arc tube. The acoustic resonances in turn generate a fluid flow that is caused by the acoustic streaming effect. Here, we present a 3D multiphysics model to determine the influence of acoustic streaming on the temperature field in the vicinity of an acoustic eigenfrequency. In that case a transition from stable to instable behavior occurs. The model is able to predict when light flicker can be expected. The results are in very good accordance with accompanying experiments.

For energy efficiency and material cost reduction it is preferred to drive high-intensity discharge lamps at frequencies of approximately 300 kHz. However, operating lamps at these high frequencies bears the risk of stimulating acoustic resonances inside the arc tube, which can result in low frequency light flicker and even lamp destruction. The acoustic streaming effect has been identified as the link between high frequency resonances and low frequency flicker. A highly coupled 3D multiphysics model has been set up to calculate the acoustic streaming velocity field inside the arc tube of high-intensity discharge lamps. It has been found that the velocity field suffers a phase transition to an asymmetrical state at a critical acoustic streaming force. The system behaves similar to a ferromagnet near the Curie point. Furthermore, it is discussed how the model allows to investigate the light flicker phenomenon. Concerning computer resources the procedure is considerably less demanding than a direct approach with a transient model., Comment: 12 Figures