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EOL computer vision pipelines: Object Detection for Image Cropping: Multi-taxon

Produced by EOL Multitaxon Object Detection Model. Automatically crops images of snakes & lizards (Squamata), beetles (Coleoptera), frogs (Anura), and carnivores (Carnivora) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/multitaxa-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>. <ul> <li>Anura = 42646 rows</li> <li>Coleoptera = 115276 rows</li> <li>Squamata = 132680 rows</li> <li>Carnivora = 31132 rows</li> </ul>

EOL computer vision pipelines: Object Detection for Image Cropping: Chiroptera

Produced by EOL Chiroptera Object Detection Model. Automatically crops images of bats (Chiroptera) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/chiroptera-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>.   17,401 rows

EOL computer vision pipelines: Object Detection for Image Cropping: Lepidoptera

Produced by EOL Lepidoptera Object Detection Model. Automatically crops images of butterflies and moths (Lepidoptera) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/lepidoptera-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>.   608,163 rows

Fig. 13 in The endocranium of the theropod dinosaur Ceratosaurus studied with computed tomography

Fig. 13. Ceratosaurus magnicornis (MWC 1, Fruita, Colorado, Morrison Formation, Upper Jurassic). Three−dimensional digital reconstruction of the endocraniuim and associated venous structures in dorsal (A) and right lateral (B) views. Venous structures are shown in black.

Fig. 14 in The endocranium of the theropod dinosaur Ceratosaurus studied with computed tomography

Fig. 14. Ceratosaurus magnicornis (MWC 1, Fruita, Colorado, Morrison Formation, Upper Jurassic). Summary three−dimensional digital reconstruction featuring the skull overlay in grey, endocranium in violet, inner ear in yellow, pneumatic sinuses in light blue, and venous sinuses in red, in right lateral (A) and dorsal (B) views.

Fig. 8 in The endocranium of the theropod dinosaur Ceratosaurus studied with computed tomography

Fig. 8. Struthio camelus (LSU−SVM no number, Recent). Axial T1 Gradient echo MRI of showing branches of trigeminal nerve. A. Maxillary and mandibular branch trigeminal foramen in posterior orbit. Pneumatized bone of the skull base is black. B. V1 ascending the floor of the endocranium. C. Optic nerves (II) just distal to the optic chiasm and V1 in the posterior orbital wall. D. V1 at the orbital apex on left and entering the orbit on the right. All sections are in the same scale.

Fig. 10 in The endocranium of the theropod dinosaur Ceratosaurus studied with computed tomography

Fig. 10. Ceratosaurus magnicornis (MWC 1, Fruita, Colorado, Morrison Formation, Upper Jurassic). A–D. Computed tomograms of the olfactory zone. E. Right lateral view of the whole braincase with vertical white lines indicating positions of sections A–D.

Fig. 6 in The endocranium of the theropod dinosaur Ceratosaurus studied with computed tomography

Fig. 6. Ceratosaurus magnicornis (MWC 1, Fruita, Colorado, Morrison Formation, Upper Jurassic). A–C. Computed tomograms of the trigeminal zone. D. Right lateral view of the whole braincase with semitransparent slab indicating location of the sections. Images A through C progress from caudal to rostral. The posterior margin of the common trigeminal foramen would have held the maxillary and mandibular branches of the trigeminal nerve (V2/3) while the ophthalmic branch (V1) would have traveled rostrally.

Fig. 4 in The endocranium of the theropod dinosaur Ceratosaurus studied with computed tomography

Fig. 4. Ceratosaurus magnicornis (MWC 1, Fruita, Colorado, Morrison Formation, Upper Jurassic). A–E. Computed tomograms of the otic zone. F. Right lateral view of the whole braincase with semitransparent slab indicating location of the sections.

Fig. 2 in The endocranium of the theropod dinosaur Ceratosaurus studied with computed tomography

Fig. 2. Ceratosaurus magnicornis (MWC 1, Fruita, Colorado, Morrison Formation, Upper Jurassic). A–D. Computed tomograms of the occipital zone. E. Right lateral view of the whole braincase with vertical white lines indicating positions of sections A–D.

Fig. 12 in The endocranium of the theropod dinosaur Ceratosaurus studied with computed tomography

Fig. 12. Struthio camelus (LSU−SVM no number, Recent). Coronal oblique CT image of the head, showing the relationship of the caudal nasal septal ridge along the ventral vomer to the adjacent caudal portion of the middle concha. The concavity of the septum mirrors the curve of the concha.

Fig. 5 in The endocranium of the theropod dinosaur Ceratosaurus studied with computed tomography

Fig. 5. Three dimensional reconstruction of the inner ear of Ceratosaurus magnicornis (MWC 1) in lateral (A), anterior (B), and dorsal (C) views. An arrowhead points to a reconstruction artifact at junction of anterior semicircular canal and utricle. D. A stereopair in the dorsolateral view.

Fig. 3. A in The endocranium of the theropod dinosaur Ceratosaurus studied with computed tomography

Fig. 3. A. Ceratosaurus magnicornis (MWC 1, Fruita, Colorado, Morrison Formation, Upper Jurassic), computed tomography image of the whole braincase in right lateral view with superimposed digital endocast (dark outline). The occipitofrontal angle is 98°. B. Allosaurus fragilis (UUVP 294, Cleveland−Lloyd Quarry, Morrison Formation, Jurassic) endocranial cast (from Rogers 1998). Matrix below the semicircular canals in an oval white outline represents epipharyngeal pneumatic sinuses and is not part of the endocranium. Note that endocast of Ceratosaurus is straighter than in Allosaurus.

Fig. 11 in The endocranium of the theropod dinosaur Ceratosaurus studied with computed tomography

Fig. 11. Ceratosaurus magnicornis (MWC 1, Fruita, Colorado, Morrison Formation, Upper Jurassic). Three−dimensional virtual rendering of the preserved pneumatic sinuses in right lateral (A) and dorsal (B) views.

Fig. 9. A in The endocranium of the theropod dinosaur Ceratosaurus studied with computed tomography

Fig. 9. A. Alligator mississippiensis (LDWF 904625, Recent), optic/pituitary zone; A1, axial T2 weighted MRI, showing the optic chiasm in interorbital foramen; A2, sagittal T2 weighted MR1 of the same specimen, showing the optic chiasm within the interorbital fenestrum. B. Ceratosaurus magnicornis (MWC 1, Fruita, Colorado, Morrison Formation, Upper Jurassic); B1, computed tomography image of the whole braincase in right lateral view, with semitransparent slab indicating location of the sections through optic/pituitary zone; B2–B4, sections through optic/pituitary zone that progress from caudal to rostral.

Fig. 7 in The endocranium of the theropod dinosaur Ceratosaurus studied with computed tomography

Fig. 7. Alligator mississippiensis (LDWF 904625, Recent). Coronal T2 weighted fast spin echo MRI of showing branches of the trigeminal nerve. The images progress from caudal to rostral. A. The caudalmost slice shows the trigeminal ganglion and the large mandibular branch (V3) entering the jaw muscle complex. B. Proximal maxillary branch (V2) exiting skull at apex of pterygoid space while the proximal ophthalmic branch (V1) travels towards the orbit inside the skull. C. V1 in the roof of the cavernous sinus, V2 outside the skull and prior to entering the posterior orbital floor next to the recurrent loop of the internal carotid artery. This section of the carotid artery is proximal to the anterior and posterior encephalic arteries (Burda 1969). D. V2 traversing the floor of the orbit before entering the snout. All sections are in the same scale.

Computational results data for the assoziated publication "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics"

This repository contains two Excel tables with computational results for our paper "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics". Each Excel table includes multiple worksheets, each representing different scenarios and models evaluated in our study. Worksheets Overview Each Excel table contains the following worksheets: 1. ML: Results for the "ML" big M values. 2. MH: Results for the "MH" big M values. 3. MF: Results for the "MF" big M values. 4. Path: Results for the path model. 5. FMInstances: Results from applying our models on the original Fontaine and Minner (2018) instances. Columns Description Each worksheet contains the following columns: - Name of Instance: A complex string with the identifier of the used instance. The relevant part is "_RXXX_", where XXX is the random seed used to generate the instance. - Number users: The number of users/commodities in the network. - B: The budget (always set to infinity in our instances). - GUROBI_RUNTIME: The time limit set for the computations. - Modelkind: The type of model used. Possible values are:   - INDICATOR: Compact model.   - FMbenders: Benders model from Fontaine and Minner (2018).   - ICM: Benders-like cuts.   - PathModel: Path enumeration model. - BigM computation: Time required to compute all the big M values used (not included in the time limit). - runtime: Runtime of the selected model. - BBnodes: Number of nodes in the Branch & Bound tree. - gap: Gap reported by Gurobi after reaching the time limit. - Cuts BLC: Number of Benders-like cuts included. - Time BLC: Time required for separating Benders-like cuts. - M improve BLC: Frequency of improvements to a big M when using the improved big M term in Benders-like cuts. - Mcutoff_AVE: Average (non-zero) improvement of a big M when using the improved big M term in Benders-like cuts. - Cuts FMBenders: Number of Benders cuts generated in the Fontaine and Minner (2018) model. - Time FMBenders: Time required to generate the Benders cuts in the Fontaine and Minner (2018) model. - Runtime path enum: Time required to enumerate all paths for the path-based model (not included in the time limit). - Average Num Path: Average number of paths generated for a single commodity/user. Multiply this value by the number of users to obtain the absolute number of paths generated. Note on FCP All the results found for the instances already had integer flow solutions. Additionally, we conducted experiments where we explicitly forced the solutions to be integer for the Benders-like cuts model. We observed that the runtimes remained the same, with only some natural insignificant hardware-induced fluctuations. Therefore, we omit reporting these results again. For further information or questions, please refer to our paper "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics" or contact the authors.

opencc-by-4.0Feb 2012View details →

GTSRB - German Traffic Sign Recognition Benchmark by Real-Time Computer Vision at Ruhr-Universität Bochum

<div> <div>The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011.  Our benchmark has the following properties: - Single-image, multi-class classification problem - More than 40 classes - More than 50,000 images in total - Large, lifelike database Acknowledgements: [INI Benchmark Website][1] [1]: http://benchmark.ini.rub.de/</div> </div>

opencc-by-4.0Sep 2024View details →

Replication Data for: Achieving Liquid Processors by Colloidal Suspensions for Reservoir Computing

Overview This README provides instructions for accessing and managing the SQLite database digits_channel_A.db, digits_channel_B.db, and digits_channel_C.db which contains the recordings for all the channels A, B, and C of the digital oscilloscope. The database includes a table named digits_data with information about each recording, including metadata and audio data. Database Schema Table: digits_data     id: INTEGER PRIMARY KEY     A unique identifier for each record.     person_id: INTEGER     Identifier for the person associated with the recorded digit.     digit: INTEGER     The digit recorded (0-9).     repeat: INTEGER     The repeat count of the recording.     audio: BLOB     Binary Large Object to store audio data. Prerequisites     Python 3.x     sqlite3 library (included with Python standard library)