Efficiently Generating Geometric Inhomogeneous and Hyperbolic Random Graphs

Hyperbolic random graphs (HRG) and geometric inhomogeneous random graphs (GIRG) are two similar generative network models that were designed to resemble complex real world networks. In particular, they have a power-law degree distribution with controllable exponent beta, and high clustering that can be controlled via the temperature T. We present the first implementation of an efficient GIRG generator running in expected linear time. Besides varying temperatures, it also supports underlying geometries of higher dimensions. It is capable of generating graphs with ten million edges in under a second on commodity hardware. The algorithm can be adapted to HRGs. Our resulting implementation is the fastest sequential HRG generator, despite the fact that we support non-zero temperatures. Though non-zero temperatures are crucial for many applications, most existing generators are restricted to T = 0. We also support parallelization, although this is not the focus of this paper. Moreover, we note that our generators draw from the correct probability distribution, i.e., they involve no approximation. Besides the generators themselves, we also provide an efficient algorithm to determine the non-trivial dependency between the average degree of the resulting graph and the input parameters of the GIRG model. This makes it possible to specify the desired expected average degree as input. Moreover, we investigate the differences between HRGs and GIRGs, shedding new light on the nature of the relation between the two models. Although HRGs represent, in a certain sense, a special case of the GIRG model, we find that a straight-forward inclusion does not hold in practice. However, the difference is negligible for most use cases

Der Homo Oeconomicus : wie rational gehen Menschen mit Geld um? ; viele Anleger machen systematische Fehler bei ihren Investments

Vielfältige Einschnitte im Rentensystem haben die Bedeutung der privaten Altersvorsorge in den vergangenen Jahren massiv erhöht. Neben Immobilienbesitz, Lebensversicherungen und staatlich geförderten Programmen zur privaten Vorsorge hat sich inzwischen auch die eigenverantwortliche Altersvorsorge mit Wertpapierdepots etabliert, so dass die Anzahl privater Depots in den letzten 25 Jahren von 8,0 auf 27,9 Millionen gestiegen ist. Vor diesem Hintergrund ist die Frage von zentraler Bedeutung, wie gut Anleger ihr Geld investieren

Functional conservation and divergence of color-pattern-related agouti family genes in teleost fishes

While color patterns are highly diverse across the animal kingdom, certain patterns such as countershading and stripe patterns have evolved repeatedly. Across vertebrates, agouti-signaling genes have been associated with the evolution of both patterns. Here we study the functional conservation and divergence by investigating the expression patterns of the two color-pattern-related agouti-signaling genes, agouti-signaling protein 1 (asip1) and agouti-signaling protein 2b (asip2b, also known as agrp2) in Teleostei. We show that the dorsoventral expression profile of asip1 and the role of the "stripe repressor" asip2b are shared across multiple teleost lineages and uncover a previously unknown association between stripe-interstripe patterning and both asip1 and asip2b expression. In some species, including the zebrafish (Danio rerio), these two genes show complementary and overlapping expression patterns in line with functional redundancy. Our results thus suggest how conserved and novel functions of agouti-signaling genes might have shaped the evolution of color patterns across teleost fishes.Peer reviewe

Embodied Digital Technologies: First Insights in the Social and Legal Perception of Robots and Users of Prostheses

New bionic technologies and robots are becoming increasingly common in workspaces and private spheres. It is thus crucial to understand concerns regarding their use in social and legal terms and the qualities they should possess to be accepted as 'co-workers'. Previous research in these areas used the Stereotype Content Model to investigate, for example, attributions of Warmth and Competence towards people who use bionic prostheses, cyborgs, and robots. In the present study, we propose to differentiate the Warmth dimension into the dimensions of Sociability and Morality to gain deeper insight into how people with or without bionic prostheses are perceived. In addition, we extend our research to the perception of robots. Since legal aspects need to be considered if robots are expected to be 'co-workers', for the first time, we also evaluated current perceptions of robots in terms of legal aspects. We conducted two studies: In Study 1, participants rated visual stimuli of individuals with or without disabilities and low- or high-tech prostheses, and robots of different levels of Anthropomorphism in terms of perceived Competence, Sociability, and Morality. In Study 2, participants rated robots of different levels of Anthropomorphism in terms of perceived Competence, Sociability, and Morality, and additionally, Legal Personality, and Decision-Making Authority. We also controlled for participants' personality. Results showed that attributions of Competence and Morality varied as a function of the technical sophistication of the prostheses. For robots, Competence attributions were negatively related to Anthropomorphism. Perception of Sociability, Morality, Legal Personality, and Decision-Making Authority varied as functions of Anthropomorphism. Overall, this study contributes to technological design, which aims to ensure high acceptance and minimal undesirable side effects, both with regard to the application of bionic instruments and robotics. Additionally, first insights into whether more anthropomorphized robots will need to be considered differently in terms of legal practice are given

«One prick and then it´s done»: a mixed-methods exploratory study on intramuscular injection in heroin-assisted treatment

Background Intramuscular (IM) injection of drugs is associated with high rates of injecting-related injuries and diseases. However, little is known about the role of this route of administration in heroin-assisted treatment. The aim of this study was to determine the prevalence of IM diacetylmorphine administration and associated complications as well as to explore patients’ views and opinions on the topic and the underlying reasons for this practice. Methods The research site was a Swiss outpatient treatment centre specialised in heroin-assisted treatment. We conducted in-depth interviews with two patients who intramuscularly inject diacetylmorphine. Interviews were analysed qualitatively, and emerging themes were used to develop a 38-item questionnaire on IM injections. We then offered this questionnaire to all patients in the treatment centre. Results Five main themes emerged from the in-depth interviews: poor venous access, side effects, subjective effects, procedure for IM injection, and consideration of alternatives to IM. These themes covered the rationale for using this route of administration, complications, subjective effects of IM diacetylmorphine, hygiene and safety measures as well as alternative routes of administration. Fifty-three patients filled in the questionnaire. The lifetime prevalence of IM injections was 60.4% (n = 32) and 34.4% (n = 11) of the patients stated that IM injection was their primary route of administration. No participant reported using the IM route for street drugs. The main reason for IM injections was poor vein access. Other reasons given were time saving and less risk of injuries. Complications included induration of muscle tissue and pain, whereas more severe complications like thrombosis and infections of the injection site were reported much less often. Conclusion As the population of opioid-dependent individuals is aging and the deterioration of access veins is likely to increase, the frequency of IM injecting will equally increase. Even though our data show that the IM injection of diacetylmorphine in a clinical setting is a common practice and appears to be relatively safe, research on alternative routes of administration is needed to provide potentially less harmful alternative routes of administration in heroin-assisted treatment

Efficiently generating geometric inhomogeneous and hyperbolic random graphs

Hyperbolic random graphs (HRGs) and geometric inhomogeneous random graphs (GIRGs) are two similar generative network models that were designed to resemble complex real-world networks. In particular, they have a power-law degree distribution with controllable exponent $β$ and high clustering that can be controlled via the temperature $T$. We present the first implementation of an efficient GIRG generator running in expected linear time. Besides varying temperatures, it also supports underlying geometries of higher dimensions. It is capable of generating graphs with ten million edges in under a second on commodity hardware. The algorithm can be adapted to HRGs. Our resulting implementation is the fastest sequential HRG generator, despite the fact that we support non-zero temperatures. Though non-zero temperatures are crucial for many applications, most existing generators are restricted to $T=0$. We also support parallelization, although this is not the focus of this paper. Moreover, we note that our generators draw from the correct probability distribution, that is, they involve no approximation. Besides the generators themselves, we also provide an efficient algorithm to determine the non-trivial dependency between the average degree of the resulting graph and the input parameters of the GIRG model. This makes it possible to specify the desired expected average degree as input. Moreover, we investigate the differences between HRGs and GIRGs, shedding new light on the nature of the relation between the two models. Although HRGs represent, in a certain sense, a special case of the GIRG model, we find that a straightforward inclusion does not hold in practice. However, the difference is negligible for most use cases

MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy

Recently, there has been tremendous interest in industry 4.0 infrastructure to address labor shortages in global supply chains. Deploying artificial intelligence-enabled robotic bin picking systems in real world has become particularly important for reducing stress and physical demands of workers while increasing speed and efficiency of warehouses. To this end, artificial intelligence-enabled robotic bin picking systems may be used to automate order picking, but with the risk of causing expensive damage during an abnormal event such as sensor failure. As such, reliability becomes a critical factor for translating artificial intelligence research to real world applications and products. In this paper, we propose a reliable object detection and segmentation system with MultiModal Redundancy (MMRNet) for tackling object detection and segmentation for robotic bin picking using data from different modalities. This is the first system that introduces the concept of multimodal redundancy to address sensor failure issues during deployment. In particular, we realize the multimodal redundancy framework with a gate fusion module and dynamic ensemble learning. Finally, we present a new label-free multi-modal consistency (MC) score that utilizes the output from all modalities to measure the overall system output reliability and uncertainty. Through experiments, we demonstrate that in an event of missing modality, our system provides a much more reliable performance compared to baseline models. We also demonstrate that our MC score is a more reliability indicator for outputs during inference time compared to the model generated confidence scores that are often over-confident

Co-occurring Mental Disorders in Transitional Aged Youth With Substance Use Disorders – A Narrative Review

Adolescence and emerging adulthood are often referred to as youth. Transitional psychiatry addresses this target group, which considers patients between 15 and 25 years of age. Substance use usually begins and peaks at this stage of life. Psychiatric disorders, foremost attention-deficit/hyperactivity disorder, and affective disorders, conduct disorders, and first-episodes psychosis frequently appear in early life stages. This review aims to provide a broad overview of transitional-aged youth's most common psychiatric comorbidities with substance use disorders. A literature search was conducted in Embase and Pubmed, and the main findings are described narratively. We present main findings for the following comorbidities: attention-deficit/hyperactivity disorder, conduct disorder, personality disorders, affective disorders, psychotic disorders, and the phenomena of overdose and suicidality. In conclusion, co-occurring mental health disorders are common and appear to facilitate the development of substance use disorders and exacerbate their overall course. Substance use also affects the severity and course of comorbid psychiatric disorders. Overall, data on transition-age youth with substance use disorders are highly inconsistent. Universal screening and treatment guidelines do not yet exist but should be aimed for in the future