Results 31 to 40 of about 23,935,082 (334)

Cache-Efficient Approach for Index-Free Personalized PageRank

open access: yesIEEE Access, 2023
Personalized PageRank (PPR) measures the importance of vertices with respect to a source vertex. Since real-world graphs are evolving rapidly, PPR computation methods need to be index-free and fast.
Kohei Tsuchida   +3 more
doaj   +1 more source

Content Based Status Updates [PDF]

open access: yes, 2018
Consider a stream of status updates generated by a source, where each update is of one of two types: high priority or ordinary (low priority). These updates are to be transmitted through a network to a monitor.
Najm, Elie, Nasser, Rajai, Telatar, Emre
core   +2 more sources

Comparing Halton and Sobol Sequences in Integral Evaluation

open access: yesZanco Journal of Pure and Applied Sciences, 2019
Halton and Sobol sequences are two of the most popular number sets used in quasi-Monte Carlo methods. These sequences are effectively used instead of pseudo random numbers in the evaluation of integrals.
Main Article Content Nadia A. Mohammed
doaj   +1 more source

A Feature Optimization Approach Based on Inter-Class and Intra-Class Distance for Ship Type Classification

open access: yesSensors, 2020
Deep learning based methods have achieved state-of-the-art results on the task of ship type classification. However, most existing ship type classification algorithms take time–frequency (TF) features as input, the underlying discriminative information ...
Chen Li   +4 more
doaj   +1 more source

Design and construction of the IMACS-IFU, a 2000-element integral field unit

open access: yes, 2004
The IMACS-IFU is an Integral Field Unit built for the IMACS spectrograph at the Magellan-I-Telescope at Las Campanas Observatory. It consists of two rectangular fields of 5 by 7 arcseconds, separated by roughly one arcminute.
Allington-Smith, J. R.   +3 more
core   +1 more source

Learning Representations of Natural Language Texts with Generative Adversarial Networks at Document, Sentence, and Aspect Level

open access: yesAlgorithms, 2018
The ability to learn robust, resizable feature representations from unlabeled data has potential applications in a wide variety of machine learning tasks. One way to create such representations is to train deep generative models that can learn to capture
Aggeliki Vlachostergiou   +3 more
doaj   +1 more source

Photonic lantern behaviour and implications for instrument design

open access: yes, 2014
Photonic lanterns are an important enabling technology for astrophotonics with a wide range of potential applications including fibre Bragg grating OH suppression, integrated photonic spectrographs and fibre scramblers for high resolution spectroscopy ...
Content, Robert   +3 more
core   +1 more source

The unpredictably eruptive dynamics of spruce budworm populations in eastern Canada

open access: yesPopulation Ecology, EarlyView.
We examine historical population data for spruce budworm from several locations through the period 1930–1997, and use density‐dependent recruitment curves to test whether the pattern of population growth over time is more consistent with Royama's (1984; Ecological Monographs 54:429–462) linear R(t) model of harmonic oscillation at Green River New ...
Barry J. Cooke, Jacques Régnière
wiley   +1 more source

From Google Earth Studio to Hologram: A Pipeline for Architectural Visualization

open access: yesApplied Sciences
High-resolution holographic visualization of built environments remains largely inaccessible due to the complexity and technical demands of traditional 3D data acquisition processes.
Philippe Gentet   +5 more
doaj   +1 more source

Dynamic Selection of Reliance Calibration Cues With AI Reliance Model

open access: yesIEEE Access, 2023
Understanding what an AI system can and cannot do is necessary for end-users to use the AI properly without being over- or under-reliant on it. Reliance calibration cues (RCCs) communicate an AI’s capability to users, resulting in optimizing their
Yosuke Fukuchi, Seiji Yamada
doaj   +1 more source

Home - About - Disclaimer - Privacy