Results 91 to 100 of about 522,455 (311)

Neural inverse knitting: From images to manufacturing instructions

, 2021
Motivated by the recent potential of mass customization brought by whole-garment knitting machines, we introduce the new problem of automatic machine instruction generation using a single image of the desired physical product, which we apply to machine ...
Kaspar, Alexandre, Matusik, Wojciech, Oh, Taehyun, Kellnhofer, Petr, Makatura, Liane   +4 more
core  

Active Learning and Machine Teaching for Online Learning: A Study of Attention and Labelling Cost

, 2021
Interactive Machine Learning (ML) has the potential to lower the manual labelling effort needed, as well as increase classification performance by incorporating a human-in-the loop component.
Persson, Jan A.,, Davidsson, Paul,, Agnes Tegen, Tegen, Agnes,, Paul Davidsson, Jan A. Persson   +5 more
core   +1 more source

MLIPX: machine-learned interatomic potential eXploration

Journal of Physics: Condensed Matter
Abstract The rapid advancement in machine-learned interatomic potentials (MLIPs) and the proliferation of universal MLIPs ( u MLIPs) have significantly broadened their application scope.
Fabian Zills, Sheena Agarwal, Tiago Goncalves, Srishti Gupta, Edvin Fako, Shuang Han, Imke Mueller, Christian Holm, Sandip De   +8 more
openaire   +2 more sources

Directed evolution of enzymes at the crossroads of tradition and innovation

FEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova, Andrej Mirossay, Erik Sedlak   +2 more
wiley   +1 more source

Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins

, 2018
Molecular evolution based on mutagenesis is widely used in protein engineering. However, optimal proteins are often difficult to obtain due to a large sequence space.
Tomoshi Kameda, Tomoshi Kameda (1368384), Mitsuo Umetsu, Koji Tsuda (86274), Yutaka Saito, Misaki Oikawa, Misaki Oikawa (5645792), Teppei Niide (1869838), Hikaru Nakazawa (1477585), Mitsuo Umetsu (1477588), Hikaru Nakazawa, Teppei Niide, Yutaka Saito (3863707), Koji Tsuda   +13 more
core   +2 more sources

Application of machine learning technology in field of aviation materials

Journal of Aeronautical Materials
The machine learning technology has broad prospects in the field of aerospace materials and plays an important role in material selection，design，and optimization.
HE Xuan, SONG Peng, KONG Dehao, HUANG Taihong, LI Qing   +4 more
doaj   +1 more source

Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries

Journal of Multidisciplinary Healthcare, 2023
Mohamad S Alabdaljabar,1,2 Babar Hasan,3 Peter A Noseworthy,4 Joseph F Maalouf,4,5 Naser M Ammash,4,5 Shahrukh K Hashmi5,6 1Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA; 2College of Medicine, Alfaisal University, Riyadh, Saudi Arabia;
Alabdaljabar MS, Hasan B, Noseworthy PA, Maalouf JF, Ammash NM, Hashmi SK   +5 more
doaj  

From energy provision to protein synthesis: Tunnelling nanotubes as mediators of intercellular metabolic cooperation in cancer

FEBS Open Bio, EarlyView.
The cytoskeleton‐mediated transport of mitochondria via tunnelling nanotubes restores respiration, increases ATP production, rescues cells from apoptosis, activates the AKT/mTOR signalling pathway, promotes cell migration and invasiveness, contributes to cancer progression and treatment resistance.
Stanislava Martínková, Jan Trnka
wiley   +1 more source

Long‐Term Follow‐Up of Chemotherapy‐Associated Biological Aging in Women With Early Breast Cancer

Aging and Cancer, EarlyView.
Women threated with adjuvant chemotherapy for early breast cancer have sustained long‐term increase in p16INK4a,, a robust marker of cell senescence, suggesting a chemotherapy‐associated age acceleration. p16INK4a as well as other biomarkers may identify patients at greatest risk for senescence‐related diseases of aging.
Hyman B. Muss, Alexis C. Wardell, Allison M. Deal, Kirsten A. Nyrop, Allison Ross, Yara G. Abdou, Jeffrey D. Aldrich, Lisa A. Cary, E. Claire Dees, Katherine E. Reeder‐Hayes, Emily M. Ray, Natalia Mitin, Denis Tsygankov   +12 more
wiley   +1 more source

The need for open source software in machine learning

, 2007
Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for diverse ...
Ratsch, Gunnar, Scholkopf, Bernhard, Vincent, P., LeCun, Y, Ong, C., Braun, M.L., Rasmussen, Carl Edward, Rasmussen, CE, Rätsch, G., Bengio, S., Müller, K.-R., Müller, KR, LeCunn, Yann, Muller, Klaus-Robert, Vincent , P., Pereira, Fernando, Vincent, Pascal, Bottou, Leon, Pereira, F, ,, Braun, ML, Ong, CS, Rasmussen, C.E., Smola, A, Sonnenburg, S., Holmes, G, Bengio, S, Weston, Jason, Williamson, RC, Sonnenburg, S, Williamson, Robert C., Bottou, L., Holmes, G., Ong, Cheng Soon, Pereira, F., Braun, M., Sonnenburg, Soren, Smola, Alexander, Williamson, R.C., Holmes , G., Schölkopf, B, Holmes, Geoffrey, Braun, Mikio L., Bengio, Samy, Schölkopf, B., Rasmussen, C., Williamson, R., Rätch, G, Weston, J, Smola, A., Ong, C.S., Müller, K., Bottou, L, Weston, J., Vincent, P, LeCun, Y.   +55 more
core