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opencc-by-4.0Apr 2023View details →

Learning a quantum computer's capability using convolutional neural networks

This is supplemental data and code for: D. Hothem et al., Learning a quantum computer's capability using convolutional neural networks, (to be published). This folder contains all the data and the analysis code to generate the results presented in that paper. The core data analysis routines use PyGSTi, which can be found at <a href="https://github.com/pyGSTio/pyGSTi">https://github.com/pyGSTio/pyGSTi</a>. Please direct any questions to Daniel Hothem (dhothem@sandia.gov). NOTE: This description template was borrowed from Timothy Proctor's Zenodo entry for: Scalable Randomized Benchmarking of Quantum Computers using Mirror Circuits.

opencc-by-4.0Sep 2022View details →

A computational workflow for binding free energies in Python

Dataset of distances between a host and six different ligands. The host was beta-cyclodextrin (bCD), while the ligands were phenol, benzene, aspirin, toluene, chlorobenzene and 1,3-dichlorobenzene. No bonds were frozen.  The ligand were set to move with a step of 0.25 angstrom from -26 to 26 relative to the bCD (a total of 208 distances). At each distance, a energy biasing potential \(E_{bias}\) was applied the keep two molecules in place.  \(E_{bias} = \frac{1}{2}\cdot K \cdot (R - R_0)^2\) The parameters of the ligands were taken from OpenFF while GLYCAM were used for the host bCD. All of it were applied in Python and the OpenMM framework. Starting parameters, pdb-, and sdf-files can be found in the start folder.

opencc-by-4.0Apr 2023View details →

Diversity of Expertise is Key to Scientific Impact: a Large Scale Analysis in the Field of Computer Science

This repository is companion to a research paper with the following abstract: Understanding the relationship between the composition of a research team and the potential impact of their research papers is crucial as it can steer the development of new science policies for improving the research enterprise. Numerous studies assess how the characteristics and diversity of research teams can influence their performance across several dimensions: ethnicity, internationality, size, and others. In this paper, we explore the impact of diversity in terms of the authors’ expertise and skills. To this purpose, we retrieved 114K papers in the field of Computer Science and analysed how the diversity of research fields within a research team relates to the number of citations their papers received in the upcoming 5 years. The results show that two different metrics reflecting the diversity of expertise are directly associated with the number of citations. This suggests that, at least in Computer Science, diversity of expertise is key to scientific impact.

FIG. 9 in Validation of hemodynamic stress calculation in coronary computed tomography angiography versus intravascular ultrasound.

FIG. 9. Violin plots representing body sizes for males and females Mexican Burrowing Toads (Rhinophrynus dorsalis) across the 4 Mexican states that had Ž5 males and 5 females. Sample sizes are presented above each plot.

FIG. 8 in Validation of hemodynamic stress calculation in coronary computed tomography angiography versus intravascular ultrasound.

FIG. 8. Monthly distribution of body sizes for 36 females, 107 males, and 266 juveniles of the Mexican Burrowing Toad (Rhinophrynus dorsalis) from México. Points are jittered for visual clarity between overlapping values.

FIG. 10 in Validation of hemodynamic stress calculation in coronary computed tomography angiography versus intravascular ultrasound.

FIG. 10. The relationship between body size and latitude in the Mexican Burrowing Toad (Rhinophrynus dorsalis) from México (n = 163). Grey shading indicates the 95% confidence interval. The equation for the line: y = 65.2 - 0.95x.

FIG. 6 in Validation of hemodynamic stress calculation in coronary computed tomography angiography versus intravascular ultrasound.

FIG. 6. Monthly ovarian cycle of female Mexican Burrowing Toads (Rhinophrynus dorsalis) from México. See text for details on reproductive stages.

FIG. 5 in Validation of hemodynamic stress calculation in coronary computed tomography angiography versus intravascular ultrasound.

FIG. 5. Monthly frequency of food presence in the stomach, extensive fat in the body cavity, and well-developed livers in the Mexican Burrowing Toad (Rhinophrynus dorsalis) from México by male (A), female (B), and ovarian stage (C). Asterisks (*) next to a month indicate that no specimens were available for examination.

FIG. 4 in Validation of hemodynamic stress calculation in coronary computed tomography angiography versus intravascular ultrasound.

FIG. 4. Monthly distribution of testis length and width as a percentage of male body size in 107 Mexican Burrowing Toads (Rhinophrynus dorsalis) from México. Points are jittered for visual clarity between overlapping values.

FIG. 2 in Validation of hemodynamic stress calculation in coronary computed tomography angiography versus intravascular ultrasound.

FIG. 2. Monthly incidence of Mexican Burrowing Toads (Rhinophrynus dorsalis) captures from México based on museum specimens and community science observations. (A) All specimen records available on VertNet that possessed month of capture data. (B) Specimens examined in this study separated by sex and life stage, which included 495 individual tadpoles (June and July) collected as lots cataloged under single specimen tags, and a series of 48 juveniles (November) cataloged in the same manner. Monthly rainfall (mean ± standard error) displayed on the third axis. Precipitation values were taken from the Mexican states where 90% of our specimens were collected (Guerrero, Veracruz, Tabasco, and Chiapas). (C) All Research Grade observations (i.e., verified by an independent observer) recorded as on iNaturalist.

FIG. 3 in Validation of hemodynamic stress calculation in coronary computed tomography angiography versus intravascular ultrasound.

FIG. 3. Body size distributions of female and male Mexican Burrowing Toads (Rhinophrynus dorsalis) from México. Violin plots representing body sizes for all males and females in our sample. Sample sizes are presented above each plot. Dashed line indicates the sample mean. Filled circles represent gravid females (ovarian stage 4).

FIG. 1 in Validation of hemodynamic stress calculation in coronary computed tomography angiography versus intravascular ultrasound.

FIG. 1. Geographic origin of museum specimens in México of the Mexican Burrowing Toad (Rhinophrynus dorsalis) examined in this study. Sample sizes are given in parentheses after the Mexican state. Bottom left: Picture of R. dorsalis by M. Pingleton with permission.

FIG. 7 in Validation of hemodynamic stress calculation in coronary computed tomography angiography versus intravascular ultrasound.

FIG. 7. The relationship between clutch size and body size in 12 female Mexican Burrowing Toads (Rhinophrynus dorsalis) from México. Grey shading indicates the 95% confidence interval. The equation for the line: y = -6,100 + 170x. Silhouettes modified from Eisermann (2017) and drawn to scale relative to each other.

Figure 3 in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study

Figure 3. Example of Grad-CAM heatmaps obtained for Podarcis lusitanicus. The upper images show two common patterns observed in male dorsal images (also found, albeit with some differences, in females). The bottom images exhibit the patterns most frequently found in male and female head lateral images (here illustrated in two females).

Figure 2 in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study

Figure 2. Confusion matrix for male (upper) and female (lower) image classification for the two-class case based on a combination of predictions from six models. Abbreviations used: Pboc, P. bocagei; Plus, P. lusitanicus.

Highly variable (no clear pattern). All portions of the dorsal views were equally used. In head images the area around the eye, the top of the head, the snout and the throat were all used in similar proportions. P. carbonelli Variable for both views. Snout and middle of the dorsum used in dorsal view. Top of the head most frequently (but not strictly) used in lateral view. P. guadarramae Whole body used for dorsal view (but variable); either throat (most common) or ear region used in head lateral views. P. hispanicus Variable. Anterior portion of snout used more frequently than in other species for both dorsal and head lateral views. P. liolepis Highly variable. Whole body used in most dorsal images, area around the eye and throat used in head lateral views, but other patterns common. P. lusitanicus Highly variable. All parts of the dorsum used (but frequently the most posterior part); area around the ear frequently used in head lateral images. P. tunesiacus Highly variable. Dorsal area near the insertion of the posterior limbs used more frequently than in other species; different regions of the head used, often simultaneously. P. Ʋaucheri Highly variable. Different regions of dorsum (from head to the posterior region) used in dorsal images, all portions of the head, but most frequently the throat, used in lateral images. P. Ʋirescens Highly variable. All parts of both images used. Head and anterior part of the dorsum more used than in other species. in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study

Highly variable (no clear pattern). All portions of the dorsal views were equally used. In head images the area around the eye, the top of the head, the snout and the throat were all used in similar proportions. P. carbonelli Variable for both views. Snout and middle of the dorsum used in dorsal view. Top of the head most frequently (but not strictly) used in lateral view. P. guadarramae Whole body used for dorsal view (but variable); either throat (most common) or ear region used in head lateral views. P. hispanicus Variable. Anterior portion of snout used more frequently than in other species for both dorsal and head lateral views. P. liolepis Highly variable. Whole body used in most dorsal images, area around the eye and throat used in head lateral views, but other patterns common. P. lusitanicus Highly variable. All parts of the dorsum used (but frequently the most posterior part); area around the ear frequently used in head lateral images. P. tunesiacus Highly variable. Dorsal area near the insertion of the posterior limbs used more frequently than in other species; different regions of the head used, often simultaneously. P. Ʋaucheri Highly variable. Different regions of dorsum (from head to the posterior region) used in dorsal images, all portions of the head, but most frequently the throat, used in lateral images. P. Ʋirescens Highly variable. All parts of both images used. Head and anterior part of the dorsum more used than in other species.

Figure 1 in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study

Figure 1. The two image types analysed in this study (before pre-processing): above, a dorsal view; below, a head lateral image. Both images correspond to the same Podarcis Ʋaucheri s.l. male.

Highly variable. Mid-portion of the dorsum used frequently (although other areas as well). Tip of the snout used often, but area around the ear and throat are also relevant. P. carbonelli Variable. In the dorsal view, the tip of the snout is frequently used. In the head lateral view, the tip of the snout is also com- monly used, as well as the most posterior region of the head. P. guadarramae Variable. Mid portion of the dorsum and tip of the snout are the regions used more frequently in dorsal and head lateral views, respectively. P. hispanicus Variable. The head and most anterior part of the dorsum are frequently used in the dorsal view. Snout and/or top of posterior region of head used. P. liolepis Variable. Different parts of the dorsum are used, whereas the tip of the snout is used in most head lateral images. P. lusitanicus Anterior dorsum, in the dorsal view, and both snout and posterior side of the head (in head lateral views) frequently used. P. tunesiacus Variable. Tip of the snout and posterior part of the trunk more used than in other species; snout and top head region behind the eye used with some frequency. P. Ʋaucheri Highly variable. All parts of the dorsum used in dorsal images, various parts of the head (but frequently snout and throat combined) used in head lateral images. P. Ʋirescens Highly variable. All portions of the dorsum used in dorsal images, region around and behind the ear more used than in other species for head lateral images. in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study

Highly variable. Mid-portion of the dorsum used frequently (although other areas as well). Tip of the snout used often, but area around the ear and throat are also relevant. P. carbonelli Variable. In the dorsal view, the tip of the snout is frequently used. In the head lateral view, the tip of the snout is also com- monly used, as well as the most posterior region of the head. P. guadarramae Variable. Mid portion of the dorsum and tip of the snout are the regions used more frequently in dorsal and head lateral views, respectively. P. hispanicus Variable. The head and most anterior part of the dorsum are frequently used in the dorsal view. Snout and/or top of posterior region of head used. P. liolepis Variable. Different parts of the dorsum are used, whereas the tip of the snout is used in most head lateral images. P. lusitanicus Anterior dorsum, in the dorsal view, and both snout and posterior side of the head (in head lateral views) frequently used. P. tunesiacus Variable. Tip of the snout and posterior part of the trunk more used than in other species; snout and top head region behind the eye used with some frequency. P. Ʋaucheri Highly variable. All parts of the dorsum used in dorsal images, various parts of the head (but frequently snout and throat combined) used in head lateral images. P. Ʋirescens Highly variable. All portions of the dorsum used in dorsal images, region around and behind the ear more used than in other species for head lateral images.

Figure 4 in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study

Figure 4. Confusion matrix for male (upper) and female (lower) image classification for the nine-class experiment based on a combination of predictions from six models. Abbreviations used: Pboc, P. bocagei; Pcar, P. carbonelli; Phis, P. hispanicus; Plio, P. liolepis; Plus, P. lusitanicus; Pvsl, P. Ʋaucheri s.l.; Pvss, P. Ʋaucheri s.s.; Pvir, P. Ʋirescens.