Results 71 to 80 of about 2,485,409 (195)

Incremental Skip-gram Model with Negative Sampling

open access: yes, 2017
This paper explores an incremental training strategy for the skip-gram model with negative sampling (SGNS) from both empirical and theoretical perspectives.
Kaji, Nobuhiro, Kobayashi, Hayato
core   +1 more source

The MeSH-gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the Biomedical Domain

open access: yes, 2018
Eliciting semantic similarity between concepts in the biomedical domain remains a challenging task. Recent approaches founded on embedding vectors have gained in popularity as they risen to efficiently capture semantic relationships The underlying idea ...
Abdeddaïm, Saïd   +2 more
core  

Effector proteins of Funneliformis mosseae BR221: unravelling plant-fungal interactions through reference-based transcriptome analysis, in vitro validation, and protein‒protein docking studies

open access: yesBMC Genomics
Background Arbuscular mycorrhizal (AM) fungi form a highly adaptable and versatile group of fungi found in natural and man-managed ecosystems. Effector secreted by AM fungi influence symbiotic relationship by modifying host cells, suppressing host ...
Pratima Vasistha   +4 more
doaj   +1 more source

A Generalization of the Gram determinant of type A

open access: yes, 2019
The Gram determinant of type $A$ was introduced by Lickorish in his work on invariants of 3 - manifolds. We generalize the theory of the Gram determinant of type $A$ by evaluating, in the annulus, a bilinear form of non-intersecting connections in the ...
Bakshi, Rhea Palak   +3 more
core  

r-grams: Relational grams

open access: yes, 2007
We introduce relational grams (r-grams). They upgrade n-grams for modeling relational sequences of atoms. As n-grams, r-grams are based on smoothed n-th order Markov chains. Smoothed distributions can be obtained by decreasing the order of the Markov chain as well as by relational generalization of the r-gram.
Landwehr, Niels, De Raedt, Luc
openaire   +1 more source

Riemannian Optimization for Skip-Gram Negative Sampling

open access: yes, 2017
Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in "word2vec" software, is usually optimized by stochastic gradient descent. However, the optimization of SGNS objective can be viewed as a problem of searching for
Fonarev, Alexander   +4 more
core   +1 more source

Excitation-dependent photoluminescence and Eu3+ doping effects in BaZrO3 phosphors for red-emitting solid-state lighting

open access: yesNext Materials
This study reports the successful synthesis of undoped and Eu3+ doped BaZrO3 phosphors via a high-temperature solid-state reaction technique. X-ray diffraction (XRD) measurements confirmed the phase purity and cubic structure of BaZrO3 both with and ...
Leela Kumari   +3 more
doaj   +1 more source

Curcumin-mediated synthesis of cuprous oxide nanoparticles and its photocatalytic application

open access: yesNext Materials
Cuprous oxide (Cu2O) has attracted significant interest due to its unique properties, including high electrical conductivity, excellent catalytic activity, and applications in catalysis, antimicrobial applications, electronics, sensors, biomedical ...
Ravi Ranjan, Madhulata Shukla
doaj   +1 more source

Inventarisasi tumbuhan mangrove di Hutan Lindung Tanjung Prapat Muda, Kecamatan Batu Ampar, Kalimantan Barat

open access: yesJurnal Biologi Udayana
Setiap ekosistem mangrove memiliki keanekaragaman vegetasi yang berbeda, demikian pula pada kawasan mangrove di Hutan Lindung Tanjung Prapat Muda, Desa Tanjung Harapan Kecamatan Batu Ampar.
Dea Fitri Handayani   +2 more
doaj   +1 more source

Breaking Sticks and Ambiguities with Adaptive Skip-gram

open access: yes, 2015
Recently proposed Skip-gram model is a powerful method for learning high-dimensional word representations that capture rich semantic relationships between words. However, Skip-gram as well as most prior work on learning word representations does not take
Bartunov, Sergey   +3 more
core   +1 more source

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