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96 results for “Singularity”
Data for: Cardiac and Respiratory Self-Gating in Radial MRI using an Adapted Singular Spectrum Analysis (SSA-FARY)
<p>Magnetic Resonance Imaging measurement data used in our paper about self-gating with SSA-FARY (DOI: <a href="https://doi.org/10.1109/TMI.2020.2985994">10.1109/TMI.2020.2985994</a>). Cardiac data was obtained from eight volunteers with no known illness using single-slice radial (SS), simultaneous multi-slice radial (SMS), and stack-of-stars (SoS) FLASH and bSSFP sequences and is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p>
Data from: The macroevolutionary singularity of snakes
<p>Snakes and lizards (Squamata) represent a third of terrestrial vertebrates and exhibit spectacular innovations in locomotion, feeding, and sensory processing. However, the evolutionary drivers of this dramatic radiation remain poorly known. We infer potential causes and ultimate consequences of squamate macroevolution by combining individual-based natural history observations (>60,000 animals) with a comprehensive time-calibrated phylogeny that we anchored with genomic data (5,400 loci) from 1,018 species. Through concerted changes in the dynamics of phenotypic evolution and speciation, snakes have transformed the macroecological landscape through their impact on the trophic architecture of animal communities. Squamate biodiversity reflects a legacy of singular events that occurred during the early history of snakes and reveals the profound impact of historical contingency on vertebrate biodiversity.</p>
Data for "Engineering Higgs dynamics by spectral singularities"
<p>These files contain the data for the article "Engineering Higgs dynamics by spectral singularities" (<a href="https://doi.org/10.48550/arXiv.2205.06826">https://doi.org/10.48550/arXiv.2205.06826</a>).</p> <p>In each .zip file, it is possible to find not only the relevant data, but also a script (.sh file extension) to generate each one of the 5 figures shown in the paper and Supplemental Information Material. The file "Phasediagram.zip" contains the data corresponding to the dynamical phase diagrams (Fig. 1 of the manuscript) for the model of flat and graphene-like density of states (DOS).</p> <p>The data for the representative dynamical phases and Fourier Transforms for the constant DOS and graphene-like DOS model can be found in the files "Dynamics_flatDOS.zip" and "Dynamics_grapheneDOS.zip" respectively. </p> <p>The Fourier Transforms of the x-component of pseudospins texture are stored in the files "FFT_x-componentofpseudospintexture_flatDOS.zip" and "FFT_x-componentofpseudospintexture_grapheneDOS.zip" for the flat DOS and graphene DOS case respectively.</p> <p>For all the .txt files inside the .zip files, we have added at the top of each column a brief description of the data recorded below. Some notations have been used and read as follows: "Delta" indicates the superconducting order parameter, and "\xi_k" means the quasiparticle energy.</p>
Dataset for Direct Geometric Probe of Singularities in Band Structure
<p>Included here is the processed data illustrated in the figures of both the main text, and the supplemental material. Below is a description of each file's contents.</p> <p> </p> <p><strong>Figure2Dcode.m</strong> contains the MATLAB code that generates Figure 2D of the main text. It takes the band populations inferred from five iterations of measurements, and calculates the means and standard errors for data taken at each theta as defined in the main text.</p> <p> </p> <p><strong>Figure2Ddata.csv</strong> contains the data illustrated in Figure 2D of the main text. The data provided are normalized band populations, such that the value 1 corresponds to the entire atom number in the sample. The rows provide the band index; the first row of data corresponds to the n=1 band, the second row corresponds to the n=2 band, etc. The columns provide the measured turning angle in units of radians; the first column corresponds to a turning angle of zero, and the angle is incremented by pi/12 radians for each column that follows. Row 5 is the error for the n=1 population, row 6 is the error for the n=2 population, and row 7 is the error on the sum of the population in bands with index not equal to 1 or 2.</p> <p> </p> <p><strong>Figure3Bcode.ipynb</strong> contains the jupyter notebook that generates Figure 3B of the main text. It takes the band populations inferred from four iterations of measurements, and calculates the means and standard errors for data taken for each intermediate point along K - M - K'. For this plot, the x-axis is chosen to be the intermediate quasi-momenta, and different colors are used to differentiate between different acceleration times.</p> <p> </p> <p><strong>Figure3Bdata.csv</strong> contains the data illustrated in Figure 3B of the main text. The five columns correspond to the five different trajectory evolution times (0.5, 0.9, 1.3, 1.7, 2.1 milliseconds) shown in the Figure 3B. The first nine rows correspond to the nine trajectory midpoint positions in the Brillouin zone, as showed in Figure 3A; the first row corresponds to a midpoint at <strong>K</strong>. The next nine rows are the errors on the measurements.</p> <p> </p> <p><strong>Figure4Ccode.m</strong> contains the MATLAB code that generates Figure 4C of the main text. It takes the band populations inferred from twelve iterations of measurements, each at a different theta as defined in the main text, and calculate the means and standard errors for data taken at each theta.</p> <p> </p> <p><strong>Figure4Cdata.csv </strong>contains the data illustrated in Figure 4C of the main text. The data provided are normalized band populations, such that the value 1 corresponds to the entire atom number in the sample. The rows provide the band index; the first row of data corresponds to the n=1 band, the second row corresponds to the n=2 band, etc. The columns provide the measured turning angle in units of radians; the first column corresponds to a turning angle of zero, and the angle is incremented by pi/6 radians for each column that follows. Row 11 is the error for the n=3 population, row 12 is the error for the n=4 population, and row 13 is the error on the sum of the population in bands with index not equal to 3 or 4.</p> <p> </p> <p><strong>FigureS3Bcode.m</strong> contains the MATLAB code that generates Figure S3B of the main text. It takes the band populations inferred from seven iterations of measurements, and calculates the means and standard errors for data taken for each hold time at quasi-momentum Q as defined in the main text. The result is then fitted to a sine with exponentially decaying envelope.</p> <p><strong>push_ramp.py </strong>(in<strong> Full Hamiltonian simulation.zip</strong>) starts with an initial state and evolves it according to the discretized schrödinger equation along the path in q-space. The Hamiltonian is calculated in <strong>basic_fcts.py</strong>. The final state is projected on the eigenstates at the final q to extract the band population. Different time intervals are used to obtain all the data. A decay to account for coherence loss is added.</p> <p> </p> <p><strong>FigureS3data.csv </strong>contains the data illustrated in Figure 3 of the supplementary material. The first row is the data values, and the second row are the error bars.</p> <p> </p> <p><strong>FigureS4Bcode.m</strong> contains the MATLAB code that generates Figure S4B of the main text. It takes the band populations inferred from four iterations of measurements, and calculates the means and standard errors for data taken for each intermediate point along K - M - K'. For this plot, the x-axis is chosen to be acceleration time, and different colors are used to differentiate between different intermediate points.</p> <p><strong>push_ramp.py </strong>(in<strong> Full Hamiltonian simulation.zip</strong>) starts with an initial state and evolves it according to the discretized schrödinger equation along the paths in q-space. The Hamiltonian is calculated in <strong>basic_fcts.py</strong>. The final state is projected on the eigenstates at the final q to extract the band population. Different time intervals are used to obtain all the data.</p> <p> </p> <p><strong>FigureS4data.csv </strong>contains the data illustrated in Figure 4 of the supplementary material. The first five rows are the normalized ground band population for five different trajectory mid points on the <strong>K</strong> - <strong>M</strong> - <strong>K'</strong> line of the Brillouin zone.; the first row is for a midpoint at <strong>K</strong>, and the fifth row is for a midpoint at <strong>M</strong>. The columns give the trajectory traversal times; the first column corresponds to a traversal time of 0.1 ms and each column corresponds to a new traversal time incremented by 0.2 ms. Rows 6-10 are the error bars for the measurements.</p> <p> </p> <p><strong>FigureS5Bcode.zip</strong> contains the codes that generate Figure S5B of the main text. For each subplots in Fig.S5B, the corresponding MATLAB code in the zip file takes the band populations inferred from three iterations of measurements, and calculate the means and standard errors for data taken at each acceleration time.</p> <p> </p> <p><strong>FigureS5Bdata.csv </strong>contains the data illustrated in Figure 5B of the supplementary material. Rows 1-20 correspond to subpanel (iii) in the Figure S5 of the supplementary material. Rows 21-40 correspond to subpanel (ii) in the Figure S5 of the supplementary material. Rows 31-60 correspond to subpanel (i) in the Figure S5 of the supplementary material.</p> <p>Rows 1-10 correspond to the band index and give the normalized band population; row 1 corresponds to band index n=1 and row 10 corresponds to band index n=10. Rows 21-30 correspond to the band index and give the normalized band population; row 21 corresponds to band index n=1 and row 30 corresponds to band index n=10. Rows 41-50 correspond to the band index and give the normalized band population; row 41 corresponds to band index n=1 and row 50 corresponds to band index n=10.</p> <p>Rows 11-20 (31-40) [51-60] give the error in the band populations for measurements in panel iii (ii) [i].</p> <p> </p> <p><strong>FigureS5Cdata.csv </strong>contains the data illustrated in Figure 5C of the supplementary material. The first (second) column is the vertical (horizontal) axis. The fourth (third) column is the error in the points on the vertical (horizontal) axis.</p> <p> </p> <p><strong>push_ramp.py </strong>(in<strong> Full Hamiltonian simulation.zip</strong>) starts with an initial state and evolves it according to the discretized schrödinger equation along the path in q-space. The Hamiltonian is calculated in <strong>basic_fcts.py</strong>. In figure S6A, at each point in time shown the state is projected onto the instantaneous eigenbasis and the different band populations are extracted. In figure S6B and figure S6C, the whole experiment sequences corresponding to figure 2 and figure 4 in the main text are simulated, and the final population obtained is plotted, with the measurement results copied for reference.</p> <p> </p> <p><strong>FigureS7code.nb</strong> contains the mathematica notebook that generate Figure S7 of the main text. This code uses the two-band model described in the supplemental material to perform simulation.</p> <p> </p> <p><strong>Image_fitting.zip</strong> contains the MATLAB code and functions that were used to analyze the band mapping images. <strong>multiboxFit_v7_1.m</strong> is the main code that uses other MATLAB functions in the zip file. Overall, it takes absorption images as input, finds the position of each peak (<strong>BoxGenerator_v1_0.m</strong>), fit for the population in each peak in the images (<strong>createFit2D.m</strong>), assign the correct band number given the final quasi-momentum in the sequence (<strong>BoxesBandsThing_v2.m</strong>), and finally plot the inferred band populations, along with a visualization of the original images overlain with a Brillouin zone (<strong>PlotBZ_v2.m</strong>). The result of fits are saved in a separate file that are accessed by other analysis codes. Figure S2 and Figure S5C are also generated with this code.</p> <p> </p> <p>Additional codes <strong>Q_path_BZ.py</strong>,<strong> group_velo.py </strong>&<strong> diffr_img.py</strong> are included in<strong> Full Hamiltonian simulation.zip</strong> to ensure the correct functionality of the codes included.</p>
FIG. 5 in Novelties in Erythroxylum P.Browne (Erythroxylaceae) from the Comoros Archipelago: two new, range-restricted and threatened species, and notes on the Mount Choungi biogeographical singularity
FIG. 5. — Distribution of Erythroxylum choungiense E.Bidault, Traclet & M.Pignal, sp. nov. (♦) and E. labatii E.Bidault & M.Pignal, sp. nov. (●) in the Comoros archipelago.
FIG. 4. — Erythroxylum labatii E.Bidault & M in Novelties in Erythroxylum P.Browne (Erythroxylaceae) from the Comoros Archipelago: two new, range-restricted and threatened species, and notes on the Mount Choungi biogeographical singularity
FIG. 4. — Erythroxylum labatii E.Bidault & M.Pignal, sp. nov.: A, stem and immature fruits; B, inflorescence with flower buds and stem portion with cataphylls; C, long-styled flower; D, mature fruit; E, short-styled flower; A, D, photos by J.-N. Labat, B, E, photos by E. Bidault, C, photo by G. Viscardi.
FIG. 1. — Erythroxylum choungiense E in Novelties in Erythroxylum P.Browne (Erythroxylaceae) from the Comoros Archipelago: two new, range-restricted and threatened species, and notes on the Mount Choungi biogeographical singularity
FIG. 1. — Erythroxylum choungiense E.Bidault, Traclet & M.Pignal, sp. nov.: A, habit (dry); B, habit (fresh); C, detail of leaf venation; D, long-styled flower; E, shortstyled flower; F, short-style flower with corolla fallen; G, mature fruit; H, seed; A, C, D-F, Guiot 03 (MAO), B, G, H, no voucher. Drawings by Ludivine Longou. Scale bars: A, B, 2 cm; C, 3 mm; D, E, 1 mm; F, 2 mm; G, H, 1 mm.
FIG. 3. — Erythroxylum labatii E.Bidault & M in Novelties in Erythroxylum P.Browne (Erythroxylaceae) from the Comoros Archipelago: two new, range-restricted and threatened species, and notes on the Mount Choungi biogeographical singularity
FIG. 3. — Erythroxylum labatii E.Bidault & M.Pignal, sp. nov.: A, detail of stem; B, long-styled flower; C, short-styled flower with corolla fallen; D, short-styled flower; E, immature fruit; F, mature fruit; G, habit, dry; H, detail of inflorescence with flower buds; I, detail of cataphyll, abaxial side; A, C, G-I, Labat et al. 3212 (P); B, D, Bidault et al. 63 (P); E, F, Labat et al. 3787 (P). Drawings by Anne-Hélène Paradis. Scale bars: A, B, 2 mm; C, 1 mm; D, 2 mm; E, F, 3 mm; G, 2 cm; H, 2 mm; I, 1 mm.
FIG. 2. — Erythroxylum choungiense E in Novelties in Erythroxylum P.Browne (Erythroxylaceae) from the Comoros Archipelago: two new, range-restricted and threatened species, and notes on the Mount Choungi biogeographical singularity
FIG. 2. — Erythroxylum choungiense E.Bidault, Traclet & M.Pignal, sp. nov.: A, habit; B, terminal leaves with red young leaves; C, long-styled flower; D, shortstyled flowers; E, upper surface of leaves and immature fruits; F, mature fruit and seed; G, lower surface of leaves showing cordate base; A, B, E-G, photos by S. Traclet, C, D, photos by V. Guiot.
Dataset: Singularity Future Technology Ltd. (SGLY) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Singular Genomics Systems, Inc. (OMIC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
FIGURE 1. 1-2 in Who's that girl? A singular Tropiduchidae planthopper from the Eocene Baltic amber (Hemiptera: Fulgoromorpha)
FIGURE 1. 1-2, FT-IR spectra of the amber specimen MAI UG 508762: FT-IR spectrum (1) and ATR corrected FT-IR spectrum with baseline corrected (2). 3-4, Piece of amber MAI UG 508762 with inclusion of Gedanotropis sontagae gen. et sp. nov., lower (3) and upper (4) sides.
FIGURE 4 in Who's that girl? A singular Tropiduchidae planthopper from the Eocene Baltic amber (Hemiptera: Fulgoromorpha)
FIGURE 4. Gedanotropis sontagae gen. et sp. nov. 1, Anterior portion of the body; 2, head in lateral view; 3, habitus of the specimen; 4, tegmen and hind wing; 5, body in lateroventral view; and 6, female genital block (ventrolateral view).
FIGURE 3 in Who's that girl? A singular Tropiduchidae planthopper from the Eocene Baltic amber (Hemiptera: Fulgoromorpha)
FIGURE 3. Gedanotropis sontagae gen. et sp. nov. tegmen venation with veins, cells and areas indicated.
FIGURE 2 in Who's that girl? A singular Tropiduchidae planthopper from the Eocene Baltic amber (Hemiptera: Fulgoromorpha)
FIGURE 2. Gedanotropis sontagae gen. et sp. nov. 1, Head, pronotum and mesonotum, dorsal view; 2, face, ventrolateral view; 3, tegmen (venation partly reconstructed); 4, hind wing (visible portion); 5, left hind leg; and 6, female genital block.
Linked collectors and determiners for: Novelties in Erythroxylum P. Browne (Erythroxylaceae) from the Comoros Archipelago: two new, range-restricted and threatened species, and notes on the Mount Choungi biogeographical singularity.
Natural history specimen data linked to collectors and determiners held within, "Novelties in Erythroxylum P. Browne (Erythroxylaceae) from the Comoros Archipelago: two new, range-restricted and threatened species, and notes on the Mount Choungi biogeographical singularity". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/0855d04a-b2c7-4fd0-90d4-efcdf49b11fd">https://bionomia.net/dataset/0855d04a-b2c7-4fd0-90d4-efcdf49b11fd</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/0855d04a-b2c7-4fd0-90d4-efcdf49b11fd">https://gbif.org/dataset/0855d04a-b2c7-4fd0-90d4-efcdf49b11fd</a>. Formatted as a Frictionless Data package.
Transport signatures of Van Hove singularities in mesoscopic twisted bilayer graphene
<p>Magic-angle twisted bilayer graphene exhibits quasi-flat low-energy bands with Van Hove singularities close to the Fermi level. These singularities play an important role in the exotic phenomena observed in this material, such as superconductivity and magnetism, by amplifying electronic correlation effects. In this work, we study the correspondence of four-terminal conductance and the Fermi surface topology as a function of the twist angle, pressure, and energy in mesoscopic, ballistic samples of small-angle twisted bilayer graphene. We establish a correspondence between features in the wide-junction conductance and the presence of van Hove singularities in the density of states. Moreover, we identify additional transport features, such as a large, pressure-tunable minimal conductance, conductance peaks coinciding with non-singular band crossings, and unusually large conductance oscillations as a function of the system size. Our results suggest that twisted bilayer graphene close the magic angle is a unique system featuring simultaneously large conductance due to the quasi-flat bands, strong quantum non-linearity due to the Van Hove singularities and high sensitivity to external parameters, which could be utilized in high-frequency device applications and sensitive detectors.</p> <p>The provided repository contains all data and scripts to reproduce the figures of the manuscript. In the new version of the repository we also provide scripts to create finite samples for conductance calculations and to create periodic samples for band structure calculations.</p>
Figures 2–5 in One size doesn′t fit all: Singularities in bat species richness and activity patterns in wind-energy complexes in Brazil and implications for environmental assessment
Figures 2–5. Bat activity based on the pooled number of echolocation pulses per hour in four wind-energy complexes in northeastern Brazil, from September 2015 to January 2017: (2) Curva dos Ventos, municipality of Caetité, state of Bahia; (3) Cristal, municipality of Morro do Chapéu, state of Bahia; (4) Modelo, municipality of João Câmara, state of Rio Grande do Norte; (5) Fonte dos Ventos, municipality of Tacaratu, state of Pernambuco.
Figure 1 in One size doesn′t fit all: Singularities in bat species richness and activity patterns in wind-energy complexes in Brazil and implications for environmental assessment
Figure 1. Wind-energy complexes in northeastern Brazil studied for the presence and activity of insectivorous bats from September 2015 to January 2017.
Data from: The macroevolutionary singularity of snakes
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