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Sea level rise (SLR) will exert pressures on assets with social value, including things such as infrastructure and habitats, in the coastal zone. Assessing and ranking the vulnerability of those assets can provide insights that support planning and projects that can reduce those vulnerabilities. In this study, we develop a quantitative, data-drive framework for calculating a sea level rise vulnerability score, using publicly available spatial data, for 111,239 parcels in Puget Sound, Washington State, USA. Notably, our approach incorporates an assessment of coastal erosion, as well as coastal flooding, in an evaluation of the exposure of each parcel, and impacts to habitats are quantified alongside impacts to existing infrastructure. The results suggest that sea level rise vulnerability in Puget Sound is widely distributed, but the overall distribution of scores is heavily skewed, suggesting that adaptation actions directed at a relatively small number of parcels could yield significant reductions in vulnerability. The results are also coupled with a concurrently developed social vulnerability index, which provides additional insight regarding those people and places that may be predisposed to adverse impacts from SLR-related hazards. We find that the proposed approach offers advantages in terms of advancing equitable SLR-related risk reduction, but also that the results should be carefully interpreted considering embedded assumptions and data limitations. 

As deep-learning based image and video manipulation technology advances, the future of truth and information looks bleak. In particular, Deepfakes, wherein a person’s face can be transferred onto the face of someone else, pose a serious threat for potential spread of convincing misinformation that is drastic and ubiquitous enough to have catastrophic real-world consequences. To prevent this, an effective detection tool for manipulated media is needed. However, the detector cannot just be good, it has to evolve with the technology to keep pace with or even outpace the enemy. At the same time, it must defend against different attack types to which deep learning systems are vulnerable. To that end, in this paper, we review various methods of both attack and defense on AI systems, as well as modes of evolution for such a system. Then, we put forward a potential system that combines the latest technologies in multiple areas as well as several novel ideas to create a detection algorithm that is robust against many attacks and can learn over time with unprecedented effectiveness and efficiency. 

The potential for climate change to exacerbate the burden of human infectious diseases is increasingly recognized, but its effects on infectious diseases of plants have received less attention. Understanding the impacts of climate on the epidemiological dynamics of plant pathogens is imperative, as these organisms play central roles in natural ecosystems and also pose a serious threat to agricultural production and food security. We use the fungal ‘flax rust’ pathogen (Melampsora lini) and its subalpine wildflower host Lewis flax (Linum lewisii) to investigate how climate change might affect the dynamics of fungal plant pathogen epidemics using a combination of empirical and modeling approaches. Our results suggest that climate change will initially slow transmission at both the within- and between-host scales. However, moderate resurgences in disease spread are predicted as warming progresses, especially if the rate of greenhouse gas emissions continues to increase at its current pace. These findings represent an important step towards building a holistic understanding of climate effects on plant infectious disease that encompasses demographic, epidemiological, and evolutionary processes. A core result is that neglecting processes at any one scale of plant pathogen transmission may bias projections of climate effects, as climate drivers have variable and cascading impacts on processes underlying transmission that occur at different scales.

 

Placental mammals had a smaller brain-to-body-size ratio after the dinosaur extinction but later developed the largest vertebrate brains. 

Abstract We report on photometric and spectroscopic observations and analysis of the 2019 superoutburst of TCP J21040470+4631129. This object showed a 9 mag superoutburst with early superhumps and ordinary superhumps, which are the features of WZ Sge-type dwarf novae. Five rebrightenings were observed after the main superoutburst. The spectra during the post-superoutburst stage showed Balmer, He i, and possible sodium doublet features. The mass ratio is derived as 0.0880(9) from the period of the superhump. During the third and fifth rebrightenings, growing superhumps and superoutbursts were observed, which have never been detected during a rebrightening phase among WZ Sge-type dwarf novae with multiple rebrightenings. To induce a superoutburst during the brightening phase, the accretion disk needs to have expanded beyond the 3 : 1 resonance radius of the system again after the main superoutburst. These peculiar phenomena can be explained by the enhanced viscosity and large radius of the accretion disk suggested by the higher luminosity and the presence of late-stage superhumps during the post-superoutburst stage, plus by more mass supply from the cool mass reservoir and/or from the secondary because of the enhanced mass transfer than those of other WZ Sge-type dwarf novae.