A note on the depth-from-defocus mechanism of jumping spiders

Jumping spiders are capable of estimating the distance to their prey relying only on the information from one of their main eyes. Recently, it has been shown that jumping spiders perform this estimation based on image defocus cues. In order to gain insight into the mechanisms involved in this blur-to-distance mapping as performed by the spider and to judge whether inspirations can be drawn from spider vision for depth-from-defocus computer vision algorithms, we constructed a three-dimensional (3D) model of the anterior median eye of the Metaphidippus aeneolus, a well studied species of jumping spider. We were able to study images of the environment as the spider would see them and to measure the performances of a well known depth-from-defocus algorithm on this dataset. We found that the algorithm performs best when using images that are averaged over the considerable thickness of the spider's receptor layers, thus pointing towards a possible functional role of the receptor thickness for the spider's depth estimation capabilities

Ideology and the limits of self-interest: System justification motivation and conservative advantages in mass politics

It is commonly assumed that political attitudes are driven by self-interest and that poor people heavily favor policies aimed at redistributing wealth. This assumption fails to explain the popularity of economic conservatism and the degree of support for the capitalist system. Such outcomes are typically explained by the suggestion that most poor people believe they will become rich one day. In a representative sample of low-income Americans, we observed that less than one-fourth were optimistic about their economic prospects. Those respondents who believed that they would become rich one day were no more likely to endorse the legitimacy of the system and no more supportive of conservative ideology or the Republican Party, compared to those who did not believe they would become rich. From a system justification perspective, we propose that people are motivated to defend the social systems on which they depend, and this confers a psychological advantage to conservative ideology. Providing ideological support for the status quo serves epistemic motives to reduce uncertainty, existential motives to reduce threat, and relational motives to share reality with members of mainstream society. We summarize evidence from the United States, Argentina, Lebanon, and other countries bearing on these propositions—including a survey administered shortly before the 2016 U.S. Presidential election—and discuss political implications of system justification motivation.Fil: Jost, John T.. University of New York; Estados UnidosFil: Langer, Melanie. University of New York; Estados UnidosFil: Badaan, Vivienne. University of New York; Estados UnidosFil: Azevedo, Flávio. Universitat Zu Köln; AlemaniaFil: Etchezahar, Edgardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; ArgentinaFil: Ungaretti, Joaquín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; ArgentinaFil: Hennes, Erin P.. Purdue University; Estados Unido

Effective Approximations for Spatial Task Allocation Problems

Although multi-robot systems have received substantial research attention in recent years, multi-robot coordination still remains a difficult task. Especially, when dealing with spatially distributed tasks and many robots, central control quickly becomes infeasible due to the exponential explosion in the number of joint actions and states. We propose a general algorithm that allows for distributed control, that overcomes the exponential growth in the number of joint actions by aggregating the effect of other agents in the system into a probabilistic model, called subjective approximations, and then choosing the best response. We show for a multi-robot grid-world how the algorithm can be implemented in the well studied Multiagent Markov Decision Process framework, as a sub-class called spatial task allocation problems (SPATAPs). In this framework, we show how to tackle SPATAPs using online, distributed planning by combining subjective agent approximations with restriction of attention to current tasks in the world. An empirical evaluation shows that the combination of both strategies allows to scale to very large problems, while providing near-optimal solutions

Effective Approximations for Multi-Robot Coordination in Spatially Distributed Tasks

Although multi-robot systems have received substantial research attention in recent years, multi-robot coordination still remains a difficult task. Especially, when dealing with spatially distributed tasks and many robots, central control quickly becomes infeasible due to the exponential explosion in the number of joint actions and states. We propose a general algorithm that allows for distributed control, that overcomes the exponential growth in the number of joint actions by aggregating the effect of other agents in the system into a probabilistic model, called subjective approximations, and then choosing the best response. We show for a multi-robot grid-world how the algorithm can be implemented in the well studied Multiagent Markov Decision Process framework, as a sub-class called spatial task allocation problems (SPATAPs). In this framework, we show how to tackle SPATAPs using online, distributed planning by combining subjective agent approximations with restriction of attention to current tasks in the world. An empirical evaluation shows that the combination of both strategies allows to scale to very large problems, while providing near-optimal solutions

Norm-based Governance for a New Era: Lessons from Climate Change and COVID-19

The world has surpassed three million deaths from COVID-19, and faces potentially catastrophic tipping points in the global climate system. Despite the urgency, governments have struggled to address either problem. In this paper, we argue that COVID-19 and anthropogenic climate change (ACC) are critical examples of an emerging type of governance challenge: severe collective action problems that require significant individual behavior change under conditions of hyper- partisanship and scientific misinformation. Building on foundational political science work demonstrating the potential for norms (or informal rules of behavior) to solve collective action problems, we analyze more recent work on norms from neighboring disciplines to offer novel recommendations for more difficult challenges like COVID-19 and ACC. Key insights include more attention to (1) norm-based messaging strategies that appeal to individuals across the ideological spectrum or that reframe collective action as consistent with resistant subgroups’ pre-existing values, (2) messages that emphasize both the prevalence and the social desirability of individual behaviors required to address these challenges, (3) careful use of public policies and incentives that make individual behavior change easier without threatening norm internalization, and (4) greater attention to epistemic norms governing trust in different information sources. We conclude by pointing out that COVID-19 and climate change are likely harbingers of other polarized collective action problems that governments will face in the future. By connecting work on norms and political governance with a broader, interdisciplinary literature on norm psychology, motivation, and behavior change, we aim to improve the ability of political scientists and policy makers to respond to these and future collective action challenges

Use of a hydrophilic coating wire reduces significantly the rate of central vein punctures and the incidence of pneumothorax in totally implantable access port (TIAP) surgery

Background: Insertion of a Totally Implantable Access Port (TIAP) can be performed either via Central Vein Puncture (CVP) or Brachiocephalic Vein Cut-down (venous section-VS). The primary success rate of TIAP implantation using VS rarely ever achieves 100%. The objective of this study was to describe a modified VS approach using a hydrophilic coated wire (TVS). Methods: From 01.01.2015 to 31.12.2015, all patients receiving TIAP implantations were screened. During this time, all patients in whom the primary VS procedure was found to be unsuccessful were analysed. Results: In 2015, 1152 patients had TIAP implantations performed by 24 different surgeons. Of these, 277 patients needed a second line rescue strategy either by CVP (n= 69) or TVS (n= 208). There were no statistically significant differences regarding demographics or indication for TIAP implantation between CVP and TVS. The operation time and the qualification of the operating surgeon between CVP and TVS did not differ significantly. After the introduction of the guidewire with a hydrophilic coated wire, the need for CVP decreased significantly from 12.7% to 8.8% (p< 0.0001). In patients receiving CVP as a second line rescue strategy, the incidence of pneumothorax (n= 3 patients (4.3%)) was significantly higher compared to patients with TVS as a second line rescue strategy (n= 1 patient (0.48%),p=0.02). Conclusion: The use of a hydrophilic coated wire significantly decreased the number of CVP and the incidence of pneumothorax. TVS is a safe and successful second-line rescue strategy