DeepPilot: A CNN for Autonomous Drone Racing

1 code implementation • Sensors 2020 • Rojas-Perez, L.O., Martinez-Carranza, J.

We argue that this helps to learn the drone’s motion trend regarding the gate, thus acting as a local memory that leverages the prediction of the flight commands.

Autonomous Navigation Navigate

Cosmic Background Removal with Deep Neural Networks in SBND

1 code implementation • 2 Dec 2020 • SBND Collaboration, R. Acciarri, C. Adams, C. Andreopoulos, J. Asaadi, M. Babicz, C. Backhouse, W. Badgett, L. Bagby, D. Barker, V. Basque, Q. Bazetto, M. Betancourt, A. Bhanderi, A. Bhat, C. Bonifazi, D. Brailsford, G. Brandt, T. Brooks, F. Carneiro, Y. Chen, H. Chen, G. Chisnall, I. Crespo-Anadón, E. Cristaldo, C. Cuesta, I., L. de Icaza Astiz, A. De Roeck, G. de Sá Pereira, M. Del Tutto, V. Di Benedetto, A. Ereditato, J. Evans, C. Ezeribe, S. Fitzpatrick, T. Fleming, W. Foreman, D. Franco, I. Furic, P. Furmanski, S. Gao, D. Garcia-Gamez, H. Frandini, G. Ge, I. Gil-Botella, S. Gollapinni, O. Goodwin, P. Green, C. Griffith, R. Guenette, P. Guzowski, T. Ham, J. Henzerling, A. Holin, B. Howard, R., S. Jones, D. Kalra, G. Karagiorgi, L. Kashur, W. Ketchum, M., J. Kim, A. Kudryavtsev, J. Larkin, H. Lay, I. Lepetic, B., R. Littlejohn, W., C. Louis, A., A. Machado, M. Malek, D. Mardsen, C. Mariani, F. Marinho, A. Mastbaum, K. Mavrokoridis, N. McConkey, V. Meddage, P. Méndez, T. Mettler, K. Mistry, A. Mogan, J. Molina, M. Mooney, L. Mora, C., A. Moura, J. Mousseau, A. Navrer-Agasson, F., J. Nicolas-Arnaldos, A. Nowak, O. Palamara, V. Pandey, J. Pater, L. Paulucci, V., L. Pimentel, F. Psihas, G. Putnam, X. Qian, E. Raguzin, H. Ray, M. Reggiani-Guzzo, D. Rivera, M. Roda, M. Ross-Lonergan, G. Scanavini, A. Scarff, D., W. Schmitz, A. Schukraft, E. Segreto, M. Soares Nunes, M. Soderberg, S. Söldner-Rembold, J. Spitz, N., J., C. Spooner, M. Stancari, V. Stenico, A. Szelc, W. Tang, J. Tena Vidal, D. Torretta, M. Toups, C. Touramanis, M. Tripathi, S. Tufanli, E. Tyley, G., A. Valdiviesso, E. Worcester, M. Worcester, G. Yarbrough, J. Yu, B. Zamorano, J. Zennamo, A. Zglam

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded.

Semantic Segmentation Data Analysis, Statistics and Probability

A disentangled generative model for disease decomposition in chest X-rays via normal image synthesis

1 code implementation • 7 Oct 2020 • Tang, Zhu, Y., Xiao, J., & Summers, R. M.

Unlike previous CXR computer-aided diagnosis works that focused on disease diagnosis/classification, we firstly propose a deep disentangled generative model (DGM) simultaneously generating abnormal disease residue maps and “radiorealistic” normal CXR images from an input abnormal CXR image.

Data Augmentation Image Generation

Variable-Drift Diffusion Models of Pedestrian Road-Crossing Decisions

1 code implementation • Computational Brain & Behavior 2021 • Pekkanen, J., Giles, O. T., Dr, Lee, Y., Madigan, R., Daimon, T., Merat, N., Markkula, G.

Human behavior and interaction in road traffic is highly complex, with many open scientific questions of high applied importance, not least in relation to recent development efforts toward automated vehicles.

Decision Making

TF-Label: a Topological-Folding Labeling Scheme for Reachability Querying in a Large Graph

1 code implementation • 1 Jun 2013 • Cheng, J., Huang, S., Wu, H., Fu, A.W.

We propose TF-label, an efficient and scalable labeling scheme for processing reachability queries.

The Exponentiated Gumbel Type-2 Distribution: Properties and Application

no code implementations • International Journal of Mathematics and Mathematical Sciences 2016 • Okorie, I. E. Akpanta, A. C. Ohakwe, J.

The new lifetime distribution is called the ExponentiatedGumbel (EG) type-2 distribution.

Vocal Bursts Type Prediction

Applications of Machine Learning in Fintech Credit Card Fraud Detection

no code implementations • IEEE International Conference on Electro Information Technology (EIT) 2021 • Lacruz, F., Saniie, J.

Fintech utilizes innovative technology to offer improved monetary administrations and financial solutions.

Fraud Detection regression

Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy

1 code implementation • Scientific Reports 2023 • Annys, A., Jannis, D. & Verbeeck, J.

Electron energy loss spectroscopy (EELS) is a well established technique in electron microscopy that yields information on the elemental content of a sample in a very direct manner.