Results 21 to 30 of about 109,902 (144)
Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem [PDF]
arXiv, 2022 Word embeddings are one of the most fundamental technologies used in natural language processing. Existing word embeddings are high-dimensional and consume considerable computational resources. In this study, we propose WordTour, unsupervised one-dimensional word embeddings.
arxiv
Powder Metallurgy and Additive Manufacturing of High‐Nitrogen Alloyed FeCr(Si)N Stainless Steel
Advanced Engineering Materials, EarlyView.The alloying element Nitrogen enhances stainless steel strength, corrosion resistance, and stabilizes austenite. This study develops austenitic FeCr(Si)N steel production via powder metallurgy. Fe20Cr and Si3N4 are hot isostatically pressed, creating an austenitic microstructure.
Louis Becker +5 more
wiley +1 more source
Active Hydrogen for Electrochemical Ammonia Synthesis
Advanced Functional Materials, EarlyView.This review provides a comprehensive overview of the active hydrogen (H*) for electrochemical ammonia synthesis with particular attention given to the regulation of H* generation and consumption to suppress the competition of hydrogen evolution reaction and enhance the yield, selectivity, and Faradaic efficiency of ammonia.
Guoqiang Gan, Guo Hong, Wenjun Zhang
wiley +1 more source
A Homeostatic Photonic Device Integrating Vapor‐Regulated Thermo‐Optical Feedback Mechanisms
Advanced Functional Materials, EarlyView.An inorganic homeostatic photonic device is designed to autonomously regulate light, temperature, and vapor sorption through integrated positive and negative feedback mechanisms at multiple wavelengths. The device uses a graded mesoporous 1D photonic crystal coupled with a photothermal layer.
Caroline Byun +4 more
wiley +1 more source
Human-in-the-Loop Refinement of Word Embeddings [PDF]
arXiv, 2021 Word embeddings are a fixed, distributional representation of the context of words in a corpus learned from word co-occurrences. Despite their proven utility in machine learning tasks, word embedding models may capture uneven semantic and syntactic representations, and can inadvertently reflect various kinds of bias present within corpora upon which ...
arxiv
Deterministic Writing of Field‐Free and Unipolar Spin‐Transfer Torque Magnetic Random‐Access Memory
Advanced Functional Materials, EarlyView.Deterministic unipolar‐switching STT‐MRAM with field‐free operation is experimentally demonstrated. The device features a compact 4F2 cell architecture using a diode as the access device and a single magnetic tunneling junction. Unlike conventional bipolar switching STT‐MRAM requiring a three‐terminal access transistor in the array, this design offers ...
Ming‐Chun Hong +22 more
wiley +1 more source
Dynamic Contextualized Word Embeddings [PDF]
arXiv, 2020 Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prior work on contextualized and dynamic word embeddings, we introduce dynamic contextualized word embeddings that represent words as a function of both linguistic and ...
arxiv
Advanced Functional Materials, EarlyView.The TOC showcases the chemical structure of the emitter tCzBT2B as an “MVP athlete” in a stadium surrounded by. spotlights and cheering fans celebrating its impressive performance in organic light‐emitting diodes (OLEDs). The stats for this emitter and its device are detailed below.
Dongyang Chen +7 more
wiley +1 more source
Engineering the Future of Restorative Clinical Peripheral Nerve Surgery
Advanced Healthcare Materials, EarlyView.What if damaged nerves could regenerate more effectively? This review unveils cutting‐edge strategies to restore nerve function, from biomaterial scaffolds and bioactive molecules to living engineered tissues. By accelerating axonal regrowth, preserving Schwann cells, and enhancing connectivity, these approaches are reshaping nerve repair—offering new ...
Justin C. Burrell +5 more
wiley +1 more source
Learning Word Sense Embeddings from Word Sense Definitions [PDF]
arXiv, 2016 Word embeddings play a significant role in many modern NLP systems. Since learning one representation per word is problematic for polysemous words and homonymous words, researchers propose to use one embedding per word sense. Their approaches mainly train word sense embeddings on a corpus.
arxiv