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FIGURES 15–19 in Two new genera of tube-making spittlebugs (Hemiptera: Machaerotidae: Enderleiniini)
FIGURES 15–19. General body features of Labramachaerota korupa sp. n. (holotype). 15. Head and pronotum, dorsal view; 16. Head, front view; 17. Anteclypeus; 18. Lateral habitus; 19. Dorsal habitus.
FIGURES 1–14 in Two new genera of tube-making spittlebugs (Hemiptera: Machaerotidae: Enderleiniini)
FIGURES 1–14. Described genera of Enderleiniini (not to scale). 1 Aecalus; 2. Apomachaerota; 3, Chaetophyes; 4. Enderleinia; 5. Hindola; 6. Labrosyne*; 7. Machaeropsis*; 8. Makiptyelus*; 9. Neuromachaerota; 10. Pectinariophyes; 11. Polychaetophyes; 12. Serreia*; 13. Taihorina; 14. Forewing of Neuroleinia (redrawn from Lallemand 1936). *Images are from Maa (1963), reproduced courtesy of the Bishop Museum Press.
FIGURES 33–34 in Two new genera of tube-making spittlebugs (Hemiptera: Machaerotidae: Enderleiniini)
FIGURES 33–34. Wing features of Kyphomachaerota maaia sp. n. (holotype). 33. Forewing (tegmen); 34. Hindwing.
Supplementary material for Impact of past steel-making activities on lanthanides and Y (REY) fractionation and potential mobility in riverbank sediments
<p>Database related to the results that are presented in the manuscript Hissler et al. that will be submitted as research paper in the journal Frontiers in Earth Science on the research topic: "Further Rare Earth Elements Environmental Dissemination: Observation, Analysis and Impacts".</p> <p>Title of the submission: Impact of past steel-making activities on lanthanides and Y (REY) fractionation and potential mobility in riverbank sediments.</p> <p>Authorship: </p> <p>Christophe Hissler<sup>1*</sup>, Emmanuelle Montarges-Pelletier<sup>2,3</sup>, Hussein J. Kanbar <sup>2,3</sup>, Mathieu Le Meur<sup>2,3,4</sup>, Christophe Gauthier<sup>2,3</sup></p> <p><sup>1</sup>CAT/ENVISION/ERIN Research Group, Luxembourg Institute of Science and Technology, L-4422 Belvaux, Luxembourg</p> <p><sup>2</sup>Université de Lorraine, CNRS, LIEC, F-54000 Nancy, France</p> <p><sup>3</sup>LTSER FRANCE Zone atelier Moselle</p> <p><sup>4</sup>SUBATECH UMR 6457/IMT Atlantique, CNRS, F-44307 Nantes, France</p> <p> </p> <p><sup>*</sup>Corresponding author: <a href="mailto:christophe.hissler@list.lu">christophe.hissler@list.lu</a></p>
Cations make a difference: Soil nutrient patches and fine-scale root abundance of individual species in a mountain grassland
<p><span>1. Root densities in the field vary at the centimetre scale, but we have no information </span><span>on </span><span>whether this variation is linked to variation in nutrient concentrations and availability. Roots of many species are able to proliferate in nutrient-rich patches in controlled conditions in culture, but because data on nutrient concentrations and, in particular, on their temporal stability in the field are scarce, we do not know to what extent root distribution in the soil bears traces of such a response.</span></p> <p><span>2. Here</span><span>,</span><span> we linked </span><span>centimetre</span><span>-scale measurements of soil nutrient concentrations over a period of 6 weeks with estimation of root biomass and its species composition at the same points to determine whether there is any association between the two. In addition to phosphorus, nitrate and ammonium, we determined</span><span> the</span><span> concentrations of metal cations (magnesium, calcium and potassium). We used qPCR to determine</span><span> the</span><span> quantities of individual species in the root biomass samples.</span></p> <p><span>3. We found that calcium and magnesium (and to a lesser degree phosphorus and potassium) showed fine-scale patchiness that was stable over the duration of the study (6 weeks) and </span><span>was</span><span> consistent over all these elements. Nutrient patches were associated with high root biomass and </span><span>the </span><span>occurrence of roots of several species. Such patches are formed primarily by elements with known low mobility in soil (cations, phosphorus). In contrast, nitrogen ions showed overwhelmingly high temporal variation with no relationship between root density and nitrogen concentration.</span></p> <p><span>4. Soil nutrient concentrations thus constitute a multidimensional signal. Some elements strongly </span><span>vary</span><span> in time, while some are much more stable and thus form stable patches that permit root response over periods of weeks or months. Among them, calcium and magnesium play an important role in forming soil heterogeneity at a scale comparable to the scale at which fine root densities also vary. As these cations affect a number of plant functions, their association with higher root densities confirms their role in growth dynamics of terrestrial ecosystems.</span></p>
Factors Affecting Managerial Decision-Making
<p>Coded segments - factors affecting managerial decision-making (IEEE Access paper: On Adopting Software Analytics for Managerial Decision-Making: A Practitioner's Perspective).</p>
(Embodied) Artificial Intelligence: making robots curious
<p><strong>The following video describes how, active vision and embodied artificial inteligence contribute to the ROMI platform. Funded by EU Grant 773875.</strong></p> <p><em>Videos are available in:</em></p> <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> <p><strong>Video script:</strong></p> <p>(MINCHIN) At Humboldt University we see how ‘embodied artificial intelligence’, ‘computer vision’ and ‘active vision’, have been used to create feedbacks between an environment and the robotic tools in motion.<br> <br> (HAFNER) Our big goal is to understand intelligence, and to understand the principles of intelligence in natural systems like humans and other animals, and to extract these principles and put them into algorithms and put and test them in robots. We think that not everything can be pre-programmed into the robot, but the robot has to learn and make experiences by the interaction of the real world. So I can show that here, so the robot is like a child randomly moving its arm around, and at the same time learning the correlation between the motor commands here and the position of the hand as the robot can see, by its own, by its own camera. That that approach is called ‘embodied artificial intelligence’ this adaptive approach is very much important for the ROMI project where we're partners in, because we can't program everything just by hand because the the conditions change you have difference, you don't know how the how the area looks like the maybe even the robot hardware changes, the motor changes and so we can use our adaptive methods and apply them to the ROMI robots so that they can really interact with the with the plants and extract information from the plants.<br> <br> (SCHILLACI) We have a system that implements a sort of artificial curiosity. The artificial curiosity drives the movement of the robot towards interesting locations. For instance one of our tasks is to support the 3d reconstruction of plants, and instead of using predefined movements of the 3d scanner around the plants, our system instead is trying to to make more intelligent movements to to discover parts of the object that perhaps are more interesting to look at.<br> <br> So we have the learning system running at the moment, so we have the artificial curiosity explorer based exploration that sends motor commands and at the same time this, so all the information that are recorded from from the robot which are images which you can see here, and the positions of the robot are sent to the model which is going to be trained. So you probably have a goal that is like; look at the tracks, like put the tripod at the centre of the image so that's probably one of the goal. And the idea is that the next step will be like to have to move around this goal so as soon as your system is well trained, so this goal could be could be changing, for instance because the plant is growing, so you need to to move these goals around to make the system adaptive. Tt's coming back again to the tripod this one.<br> <br> (HAFNER)<strong> </strong>In ROMI it's a very challenging task because we have to we have to cope with this these real systems and we have to cope with with wind and with weather and the plants growing unexpectedly and so it's very challenging, but it's also very fascinating.<br> <br> (SCHILLACI)<strong> </strong>So the next step will be having more movement capabilities, so you will be able to segment out parts of the images that you don't need. Antonio is actually working on segmenting out the interesting objects from the from the scene, and in particular also to avoid the arm to crash into plants. So you you take first a screenshot from from a camera that is located at the top of the robot and then so you segment out all the objects that you don't need, and the object also where the robot could crash so you your trajectories will be going around plans do not crash onto them.</p>
Computer vision: algorithms to make sense of the world
<p><strong>The following video describes how computer vision is used by the ROMI platform for object and species detection in both 2D and 3D, and how it is integral to the weeding tool. Funded by EU Grant 773875.</strong></p> <p><em>Videos are available in:</em></p> <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> <p><strong>Video script:</strong></p> <p>(CAMPRODON) What's computer vision? Mmm … computer vision for me is making sense of pixels. I think computer vision has a profound effect in the way that we understand the world because as humans vision is so centric right. If dogs would be making computers, maybe they wouldn't talk that much about vision. But for us it's so centric in the way that we perceive the world and the way we learn about the world, that actually I think it's easier for that of course to program and think useful ways machines could get information you know through vision. And also especially because vision is one of the much more complex senses that we have.<br> <br> (SOLLAZZO) Image and videos that represent nowadays the 80 percent of the data that we produce and the introduction of computer vision and machine learning becomes necessary to start extrapolating information out of this new source of data.<br> <br> (COLLIAUX) So just a point of clarification because we often talk about AI and so just to be a bit more precise about what we do in ROMI. Because AI is quite a vague term, and so what we do mainly is robotics and computer vision.<br> <br> (SOLLAZZO) Computer vision is at the end a limited set of tools and systems that are basically based on mathematical representation and description of the pixel that represent the image, they are part of the image, and machine learning is based on a different approach of interpretation of those pixels.<br> <br> (COLLIAUX) So the rover is for weeding and to remove the weeds you need to detect the weeds first and so we use a computer vision algorithm to detect where are the weeds where are the salads.<br> <br> (SOLLAZZO) So let's see one by one which are the methods that we implemented, in our algorithm, in our system. So we start with the feature extraction in order to do that in fact we go one by one over the images and we understand which are the pixels in common between one and the other. From these method in fact it's possible to recreate an orthomosaic view, an orthomosaic image, but afterwards we need to align it to all the previous images that we've been creating in the previous analysis. So after the generation of the orthomosaic view, what we do is that we start to cut the main image into a portion into a series of smaller portions. This facilitates the execution of the machine learning algorithm and the possibility to recognise the presence or not, of lettuce in the scene. After the recognition has been performed we put together the images once again and we can reconstruct an orthomosaic view with a detected position of the different lettuce. This is necessary to understand not only the position but also the area of growth that these different lettuce are occupying over time. From the geolocation of every single plant we start to analyse the growing curve over time. This is possible thanks to the implementation of ‘Mask RCNN’. So thanks to the generation of all these different areas that during time, will tell us the growing pattern of every single lettuce, and this will be extremely useful to understand when is the moment to harvest the plant when the plant is in fact bolting, more or less this is ok.<br> <br> (COLLIAUX) So we do what I showed was about 2d computer vision, but we do a lot of 3d computer vision also in the project and so let me show you a bit what we do with a plant scanner. So it is uh used by biologists to study the geometry of the plants so they want to reconstruct the pre-architecture of a plant and study that architecture. So for this we take many images of a plant by turning a camera in a circle around the plant, we generate a mask but again a segmentation algorithm to detect where where the plant is and where the background is, and then we can generate a point cloud by an algorithm called ‘space carving’ or ‘shape from silhouette’ which based on the many silhouettes you collected it looks for it it carves the space for the shape which is the most compatible with all the projection of the shape.<br> <br> (CAMPRODON) So what we're doing in ROMI at the end, I would say in a way we hack existing technologies, we take advantage of the low cost cameras that exist in phones right we don't need to rely anymore in high-end industrial cameras, we take advantage of the low-cost computational power, computing cheaper than ever. So these images that we take we can process them with software in ways that was not possible before, we take advantage of software, of especially of open source software and free software and then we build the training models right, so this software is capable to detect on top of that images insights, to go from data to information.</p>
Figure 5 in The cowl does not make the monk: scarab beetle pollination of the Neotropical aroid Taccarum ulei (Araceae: Spathicarpeae)
Figure 5. Non-metric multidimensional scaling (NMDS) representation of eight selected floral traits (stress = 0.088, R2 axis 1 = 0.73, R2 axis 2 = 0.42), comprising all known taxa from the Zantedeschia clade (13 genera, 26 species; taken from Gibernau et al., 2010). Each different plot marker corresponds to a particular clade or tribe, detailed in the bottom caption. Black elipses delimit the genera Dieffenbachia, Homalomena and Philodendron.
Figure 4 in The cowl does not make the monk: scarab beetle pollination of the Neotropical aroid Taccarum ulei (Araceae: Spathicarpeae)
Figure 4. Non-metric multidimensional scaling (NMDS) representation of eight selected floral traits (stress = 0.088, R2 axis 1 = 0.73, R2 axis 2 = 0.42) obtained from 69 species of the family Araceae (taken from Gibernau et al., 2010). Each different plot marker corresponds to a pollination syndrome (Chouteau et al., 2008), detailed at the bottom caption. Taccarum ulei is represented by the larger grey triangle.
Figure 2 in The cowl does not make the monk: scarab beetle pollination of the Neotropical aroid Taccarum ulei (Araceae: Spathicarpeae)
Figure 2. Taccarum ulei: A, habit; B, Cyclocephala celata (Scarabaeidae, Cyclocephalini) over pistillate flowers on day 1 of anthesis; C, Cyclocephala cearae crowding inside a thermogenic and odoriferous inflorescence on day 1 of anthesis; D, pollen-covered Cyclocephala cearae over the male zone of the spadix on day 2 of anthesis, ready to fly away; E, Melanoloma sp. (Richardiidae) visiting an inflorescence on day 2 of anthesis; F, pupae of Melanoloma sp. attached to the inner surface of the spathe. Photos: A.C.D. Maia and C. Schlindwein.
Figure 1 in The cowl does not make the monk: scarab beetle pollination of the Neotropical aroid Taccarum ulei (Araceae: Spathicarpeae)
Figure 1. Simplified phylogeny of the Zantedeschia clade, redrawn from Cusimano et al. (2011) and Gonçalves et al. (2007). Geographical information is taken from Mayo, Bogner & Boyce (1997), and pollinator records are from Gibernau (2011). Within the Spathicarpeae, the boxes outlined by dotted lines indicate taxa with biogeographical distribution essentially restricted to lowland forests. Taxa restricted to the Andean highlands are indicated by the grey box.
Figure 3 in The cowl does not make the monk: scarab beetle pollination of the Neotropical aroid Taccarum ulei (Araceae: Spathicarpeae)
Figure 3. Thermogenesis in Taccarum ulei during the 2-day flowering cycle: A, temperature curves of different zones of the spadix of one inflorescence; B, temperature differences between the middle of the male zone of the spadix and ambient air, measured from two inflorescences. The vertical grey bands represent the heating interval during the female phase (day 1).
Figure 3 Bayesian inference tree reconstructed from cytochrome b in Fossorial morphotype does not make a species in water voles
Figure 3 Bayesian inference tree reconstructed from cytochrome b sequences of water vole ArVicola. The tree is rooted with 11 species of Arvicolinae: MicrotUS aGreStiS, M. cabrerae, M. SUbterraneUS, M. lUSitanicUS, M. dUodecimcoStatUS, M. arValiS, Neodon irene, N. leUcUrUS, ChionomYS niValiS, C. roberti, and C. GUd. The branching pattern and branch lengths follow the Bayesian analysis, whereas the first and second numbers on the branches correspond to posterior probability values and bootstrap support in the maximum likelihood tree analyses, respectively. Symbols for morphotypes (∆ – fossorial; □ – aquatic) correspond to those in Figure 1 and Table 1.
Figure 2 in Fossorial morphotype does not make a species in water voles
Figure 2 Skulls (in lateral view) of fossorial (A) and aquatic (B) water voles: (A) Slovenia, Ljubljana, Blagovica, CbL =35.5 mm (PMS 18874); (B) Bosnia and Herzegovina, Kupres, CbL =39.6 mm (PMS 2541). Skulls are deposited in the Slovenian Museum of Natural History (PMS). Scale bar =5 mm.
Figure 1 in Fossorial morphotype does not make a species in water voles
Figure 1 Distribution of water vole samples sequenced for cytochrome b gene. Approximate ranges of ArVicola amphibiUS S.S. and A. Scherman follow Batsaikhan et al. (2008) and Amori et al. (2008a), respectively. Symbols for morphotypes (∆ – fossorial; □ – aquatic) correspond to those in Figure 4 and Table 1. Samples of unknown morphotype are denoted by circles.
A Venus in the Making? Predictions for JWST Observations of the Ultracool M-Dwarf Planet LP 890-9 c
<p>This repository contains the pressure-temperature profiles and volume mixing ratio profiles used in a paper accepted in MNRAS Letters under the same name.</p>
OCFI: Make Function Entry Identification Hard Again
<p>Function entry identification is a crucial yet challenging task for binary disassemblers that has been the focus of research in the past decades. However, recent researches show that call frame information (CFI) provides accurate and almost complete function entries. With the aid of CFI, disassemblers have significant improvements in function entry detection. CFI is specifically designed for efficient stack unwinding, and every function has corresponding CFI in x64 and aarch64 architectures. Nevertheless, not every function and instruction unwinds the stack at runtime, and this observation has led to the development of techniques such as obfuscation to complicate function detection by disassemblers.</p> <p>We propose a prototype of OCFI to obfuscate CFI based on this observation. The goal of OCFI is to obstruct function detection of popular disassemblers that use CFI as a way to detect function entries. We evaluated OCFI on a large-scale dataset that includes real-world applications and automated generation programs, and found that the obfuscated CFI was able to correctly unwind the stack and make the detection of function entries of popular disassemblers more difficult. Furthermore, on average, OCFI incurs a size overhead of only 4% and nearly zero runtime overhead.</p>
Supplementary material 1 from: Galán Díaz J, de la Riva EG, Martín-Forés I, Vilà M (2023) Which features at home make a plant prone to become invasive? NeoBiota 86: 1-20. https://doi.org/10.3897/neobiota.86.104039
Species list, references accessed during bibliographic research, phylogenetic inference used in the analyses, PCA of climatic variables, and results of linear regressions
Supplementary Material to "Making DL-Lite Planning Practical"
<p>Supplementary material to the paper<br> Stefan Borgwardt, Jörg Hoffmann, Alisa Kovtunova, Marcel Steinmetz: "Making DL-Lite Planning Practical" 641-645 2021 KR https://doi.org/10.24963/kr.2021/61</p> <p>It includes<br> * A PDDL pre-processor tool written in C for realizing a polynomial DNF transformation. It works in two modes: ``derived predicates'' or naive transformation.</p> <p> * Benchmark collection used in the KR21 planning & dl-lite paper. The collection contains several eKAB (original and new) domains, eKAB versions of two WSC domains, and modified PDDL benchmarks.</p> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.