Results 61 to 70 of about 226,863 (285)

Advanced Error Recovery during Top-Down Parsing [PDF]

open access: yes, 2015
Syntaktický analyzátor je jednou z nejdůležitějších částí překladače při často používaném přístupu syntaxí řízený překlad. Při tomto přístupu řídí syntaktický analyzátor sémantické akce a generování abstraktního syntaktického stromu.
Obluková, Alena
core  

Principles and Implementation of Deductive Parsing [PDF]

open access: yes, 1994
We present a system for generating parsers based directly on the metaphor of parsing as deduction. Parsing algorithms can be represented directly as deduction systems, and a single deduction engine can interpret such deduction systems so as to implement ...
Pereira, Fernando C. N.   +2 more
core   +9 more sources

Excitation Energy Transfer between Porphyrin Dyes on a Clay Surface: A Study Employing Multifidelity Machine Learning

open access: yesAdvanced Theory and Simulations, EarlyView.
Inspired by natural light‐harvesting systems, this study computationally investigates a synthetic antenna by arranging cationic free‐base porphyrin molecules on an anionic clay surface. Using a multiscale quantum mechanics/molecular mechanics (QM/MM) approach combined with a multifidelity machine learning method, excitation energies are predicted ...
Dongyu Lyu   +7 more
wiley   +1 more source

Probabilistic parsing [PDF]

open access: yes, 2011
Postprin
Nederhof, Mark Jan, Satta, Giorgio
core   +1 more source

Semaphorin 3E‐Plexin‐D1 Pathway Downstream of the Luteinizing Hormone Surge Regulates Ovulation, Granulosa Cell Luteinization, and Ovarian Angiogenesis in Mice

open access: yesAdvanced Science, EarlyView.
The Semaphorin 3E (Sema3E)‐Plexin‐D1 pathway mediated by C/EBPα and C/EBPβ downstream of the luteinizing hormone (LH) surge plays important roles in the mouse preovulatory ovary. Timely activation and suppression of this pathway during the preovulatory stage are crucial for ovulation, corpus luteum formation, and proper angiogenesis.
Hanxue Zhang   +11 more
wiley   +1 more source

Testosterone Delays Bone Microstructural Destruction via Osteoblast‐Androgen Receptor‐Mediated Upregulation of Tenascin‐C

open access: yesAdvanced Science, EarlyView.
This study reveals that Testosterone–Androgen Receptor signaling delays elderly male bone destruction by upregulation of the osteoblastic extracellular tenascin‐C (TNC). The osteoprotective effect of fibrinogen C‐terminus of TNC is demonstrated in male osteoporotic mice model that osteoblast‐specific Ar‐knockout, potentially via inhibition of ...
Yong Xie   +8 more
wiley   +1 more source

An Efficient Implementation of the Head-Corner Parser [PDF]

open access: yes, 1996
This paper describes an efficient and robust implementation of a bi-directional, head-driven parser for constraint-based grammars. This parser is developed for the OVIS system: a Dutch spoken dialogue system in which information about public transport ...
van Noord, Gertjan
core   +6 more sources

A Minimal Span-Based Neural Constituency Parser

open access: yes, 2017
In this work, we present a minimal neural model for constituency parsing based on independent scoring of labels and spans. We show that this model is not only compatible with classical dynamic programming techniques, but also admits a novel greedy top ...
Andreas, Jacob   +2 more
core   +1 more source

Targeting Neutrophil/Eosinophil Extracellular Traps by Aptamer‐Functionalized Nanosheets to Overcome Recalcitrant Inflammatory Disorders

open access: yesAdvanced Science, EarlyView.
In this study, C‐TAH, as multifunctional nanosheets, is developed with tannic acid and histone aptamer coverage. C‐TAH displays mild cytotoxicity, robust dsDNA and NETs/EETs binding efficiency, and potent antioxidant and antibacterial ability in vitro. C‐TAH treatment ameliorates dysregulated inflammation and restores hearing function in animal models,
Yongqiang Xiao   +13 more
wiley   +1 more source

Rationally Design Thermoelectric Materials Based on Ingenious Machine Learning Methods

open access: yesAdvanced Electronic Materials, EarlyView.
A machine learning framework is developed to accurately predict thermoelectric performance of materials. By combining high‐quality data, advanced feature engineering, and machine learning, the model identifies promising candidates like CsCdBr3 and TlBSe3.
Yuqing Sun   +4 more
wiley   +1 more source

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