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Algorithm.

Ramon Guevara Erra (2269819), Judit Gervain (524952) · Figshare · 2016

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Algorithm.

Jacques Demongeot (362684), Eric Goles (362685), Michel Morvan (362686) et al. · Figshare · 2015

<p>Algorithm that writes in explicit form the characteristic polynomials (with indeterminate the state perturbation rate ) of the probabilities of passage from any attraction basin to any other.</p>

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Algorithm.

Xiaolong Shi (412458), Saeed Kosari (11600845), Saira Hameed (3572894) et al. · Figshare · 2024

<div><p>Fuzzy graphs are very important when we are trying to understand and study complex systems with uncertain and not exact information. Among different types of fuzzy graphs, cubic fuzzy graphs are special due to their ability to represent the membership degree of both vertices and edges using intervals and fuzzy numbers, respectively. To figure out how things are connected in cubic fuzzy graphs, we need to know about cubic <i>α</i>−strong, cubic <i>β</i>−strong and cubic <i>δ</i>−weak edges. These concepts better help in making decisions, solving problems and analyzing things like transportation, social networks and communication systems. The applicability of connectivity and comprehension of cubic fuzzy graphs have urged us to discuss connectivity in the domain of cubic fuzzy graphs. In this paper, the terms partial cubic <i>α</i>−strong and partial cubic <i>δ</i>−weak edges are introduced for cubic fuzzy graphs. The bounds and exact expression of connectivity index for several cubic fuzzy graphs are estimated. The average connectivity index for cubic fuzzy graphs is also defined and some results pertaining to these concepts are proved in this paper. The results demonstrate that removing some vertices or edges may cause a change in the value of connectivity index or average connectivity index, but the change will not necessarily be related to both values. This paper also defines the concepts of partial cubic connectivity enhancing node and partial cubic connectivity reducing node and some related results are proved. Furthermore, the concepts of cubic <i>α</i>−strong, cubic <i>β</i>− strong, cubic <i>δ</i>−weak edge, partial cubic <i>α</i>−strong and partial cubic <i>δ</i>−weak edges are utilized to identify areas most affected by a tsunami resulting from an earthquake. Finally, the research findings are compared with the existing methods to demonstrate their suitability and creativity.</p></div>

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Algorithm.

Wei Luo (80175), Guoqing Zhang (151441), Quanbo Yuan (18554591) et al. · Figshare · 2024

<div><p>Enhanced animal welfare has emerged as a pivotal element in contemporary precision animal husbandry, with bovine monitoring constituting a significant facet of precision agriculture. The evolution of intelligent agriculture in recent years has significantly facilitated the integration of drone flight monitoring tools and innovative systems, leveraging deep learning to interpret bovine behavior. Smart drones, outfitted with monitoring systems, have evolved into viable solutions for wildlife protection and monitoring as well as animal husbandry. Nevertheless, challenges arise under actual and multifaceted ranch conditions, where scale alterations, unpredictable movements, and occlusions invariably influence the accurate tracking of unmanned aerial vehicles (UAVs). To address these challenges, this manuscript proposes a tracking algorithm based on deep learning, adhering to the Joint Detection Tracking (JDT) paradigm established by the CenterTrack algorithm. This algorithm is designed to satisfy the requirements of multi-objective tracking in intricate practical scenarios. In comparison with several preeminent tracking algorithms, the proposed Multi-Object Tracking (MOT) algorithm demonstrates superior performance in Multiple Object Tracking Accuracy (MOTA), Multiple Object Tracking Precision (MOTP), and IDF1. Additionally, it exhibits enhanced efficiency in managing Identity Switches (ID), False Positives (FP), and False Negatives (FN). This algorithm proficiently mitigates the inherent challenges of MOT in complex, livestock-dense scenarios.</p></div>

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ALGORITHM?

Multi-proposal Metropolis-hastings, Monte Carlo, Jean-françois Delmas et al. · 2009

Abstract. The waste-recycling Monte Carlo (WR) algorithm introduced by physicists is a modification of the (multi-proposal) Metropolis-Hastings algorithm, which makes use of all the proposals in the empirical mean, whereas the standard (multi-proposal) Metropolis-Hastings algorithm only uses the accepted proposals. In this paper, we extend the WR algorithm into a general control variate technique and exhibit the optimal choice of the control variate in terms of asymptotic variance. We also give an example which shows that in contradiction to the intuition of physicists, the WR algorithm can have an asymptotic variance larger than the one of the Metropolis-Hastings algorithm. However, in the particular case of the Metropolis-Hastings algorithm called Boltzmann algorithm, we prove that the WR algorithm is asymptotically better than the Metropolis-Hastings algorithm. This last property is also true for the multi-proposal Metropolis-Hastings algorithm. In this last framework, we consider a linear parametric generalization of WR, and we propose an estimator of the explicit optimal parameter using the proposals. 1.

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Algorithm

Anand Rangarajan, Alan Yuille, Eric Mjolsness · 2008

The softassign quadratic assignment algoithm is a discrete time, continuous state, synchronous updating optimizing neural network. While its effectiveness has been shown in the traveling salesman problem, graph matching and graph partitioning in thousands of simulations, there was no associated study of its convergence properties. Here, we construct discrete time Lyapunov functions for the cases of exact and approximate doubly stochastic constraint satisfaction which can be used to show convergence to a fixed point. The combination of good convergence properties and experimental success make the softassign algorithm the technique of choice for neural QAP optimization. 1

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Algorithm

Brentjes, S. · MPG.PuRe (Max Planck Society) · 2017

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algorithm

Ahmed Fouad Ali · 2015

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@algorithm

Bulletin of Miscellaneous Information (Royal Gardens Kew)

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Allington, E · UCL Discovery (University College London) · 2005

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"Algorithm"

Bethany Nowviskie · Libra · 2014

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Algorithm

Linda Sherrell · 2013

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Algorithm

Jeremy Johnson · Columbia University Press eBooks · 2021

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Jennie Malboeuf · Project Muse (Johns Hopkins University) · 2021

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Algorithm

2008

Relaxationlabellingisanoptimiza-tiontechniqueusedinmanyeldsto solveconstraintsatisfactionproblems (CSP).Thealgorithmndsacombina-tionofvaluesforasetofvariablessuch thatsatises-tothemaximumpossible degree-asetofgivenconstraints.This paperdescribessomeexperimentsper-formedapplyingittoPOStaggingand theconstraintsused.

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algorithm

M. Rezapoor Mirsaleh, M. R. Meybodi, M. Rezapoor et al. · 2014

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Algorithm

J. Wachs, Oren Shapira, Juan Wachs · 2015

A Graphical method based on the Xie-Beni Validity index to improve the ‘Possibilistic C-Means with Repulsion’

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Algorithm

W. M. Utomo, S. S. Yi, Y. M. Y Buswig et al. · 2011

This paper proposes a neural network control voltage tracking scheme of a DC-DC Flyback converter. In this technique, a back propagation learning algorithm is employed. The controller is designed to improve performance of the Flyback converter during transient and steady state operations. Furthermore, to investigate the effectiveness of the proposed controller, some operations such as starting-up and reference voltage variations are verified. The numerical simulation results show that the proposed controller has a better performance compare to the conventional PI-Controller method. Keyword:

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Algorithm.

Marie Warny (3642523), Jens Helby (3155541), Børge Grønne Nordestgaard (5920256) et al. · Figshare · 2018

<p>Absolute 2-year risks of hospitalization due to infection for different blood lymphocyte categories, stratified for age, sex, and smoking status. Nonsmokers were former smokers and never-smokers combined. Individuals diagnosed with immunodeficiency, hematologic disease, and/or autoimmune disease were not included when calculating absolute risks for the algorithm, since these individuals have a well-known high risk of infection. Numbers indicate absolute 2-year risk as rounded percentages.</p>

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Algorithm

Tarleton Gillespie · Princeton University Press eBooks · 2016

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