Search results for " algorithm"
showing 10 items of 2538 documents
An Efficient Algorithm for Helly Property Recognition in a Linear Hypergraph
2001
International audience; In this article we characterize bipartite graphs whose associated neighborhood hypergraphs have the Helly property. We examine incidence graphs both hypergraphs and linear hypergraphs and we give a polynomial algorithm to recognize if a linear hypergraph has the Helly property.
Online Hyperparameter Search Interleaved with Proximal Parameter Updates
2021
There is a clear need for efficient hyperparameter optimization (HO) algorithms for statistical learning, since commonly applied search methods (such as grid search with N-fold cross-validation) are inefficient and/or approximate. Previously existing gradient-based HO algorithms that rely on the smoothness of the cost function cannot be applied in problems such as Lasso regression. In this contribution, we develop a HO method that relies on the structure of proximal gradient methods and does not require a smooth cost function. Such a method is applied to Leave-one-out (LOO)-validated Lasso and Group Lasso, and an online variant is proposed. Numerical experiments corroborate the convergence …
Selection of suitable housekeeping genes for expression analysis in glioblastoma using quantitative RT-PCR
2009
Abstract Background Considering the broad variation in the expression of housekeeping genes among tissues and experimental situations, studies using quantitative RT-PCR require strict definition of adequate endogenous controls. For glioblastoma, the most common type of tumor in the central nervous system, there was no previous report regarding this issue. Results Here we show that amongst seven frequently used housekeeping genes TBP and HPRT1 are adequate references for glioblastoma gene expression analysis. Evaluation of the expression levels of 12 target genes utilizing different endogenous controls revealed that the normalization method applied might introduce errors in the estimation of…
Standard Vs Uniform Binary Search and Their Variants in Learned Static Indexing: The Case of the Searching on Sorted Data Benchmarking Software Platf…
2023
Learned Indexes are a novel approach to search in a sorted table. A model is used to predict an interval in which to search into and a Binary Search routine is used to finalize the search. They are quite effective. For the final stage, usually, the lower_bound routine of the Standard C++ library is used, although this is more of a natural choice rather than a requirement. However, recent studies, that do not use Machine Learning predictions, indicate that other implementations of Binary Search or variants, namely k-ary Search, are better suited to take advantage of the features offered by modern computer architectures. With the use of the Searching on Sorted Sets SOSD Learned Indexing bench…
Analysis of the IEEE 802.11e EDCA Under Statistical Traffic
2006
Many models have been proposed to analyze the performance of the IEEE 802.11 distributed coordination function (DCF) and the IEEE 802.11e enhanced distributed coordination function (EDCA) under saturation condition. To analyze DCF under statistical traffic, Foh and Zukerman introduce a model that uses Markovian Framework to compute the throughput and delay performance. In this paper, we analyze the protocol service time of EDCA mechanism and introduce a model to analyze EDCA under statistical traffic using Markovian Framework. Using this model, we analyze the throughput and delay performance of EDCA mechanism under statistical traffic.
Capacity and Energy-Consumption Optimization for the Cluster-Tree Topology in IEEE 802.15.4
2011
International audience; 802.15.4 proposes to use a cluster-tree hierar- chy to organize the transmissions in Wireless Sensor Networks. In this letter, we propose a framework to analyze formally the capacity and the energy consumption of this structure. We derive a Mixed Integer Linear Programming (MILP) formulation to obtain a topology compliant with the standard. This formulation provides the optimal solution for the network capacity: this con- stitutes an upper bound for any distributed algorithms permitting to construct a cluster-tree. This framework can also be used to evaluate the capacity and to compare quantitatively different cluster-tree algorithms.
A novel framework for MR image segmentation and quantification by using MedGA
2019
BACKGROUND AND OBJECTIVES: Image segmentation represents one of the most challenging issues in medical image analysis to distinguish among different adjacent tissues in a body part. In this context, appropriate image pre-processing tools can improve the result accuracy achieved by computer-assisted segmentation methods. Taking into consideration images with a bimodal intensity distribution, image binarization can be used to classify the input pictorial data into two classes, given a threshold intensity value. Unfortunately, adaptive thresholding techniques for two-class segmentation work properly only for images characterized by bimodal histograms. We aim at overcoming these limitations and…
A complex network analysis of inbound tourism in Sicily
2019
In this article, the complex dynamics of inbound tourism in Sicily is analyzed for the period 1998–2017. The horizontal visibility graph algorithm is used to transform the overnight stays' time series into a network whose topology is investigated by standard network analysis. Discontinuities in the domestic and international tourism demand were identified in order to detect signals of change and the timing of the directional change in tourism growth. The network degree distribution confirms the complex structure of the destination and reveals the random and thus more unpredictable nature of the international tourism demand in Sicily, compared with a more stable domestic segment. Some policy…
Magnetic Levitation – Modelling, Identification and Open Loop Verification
2020
<p>The paper describes a procedure using the first principle modelling and experimental identification of the Magnetic Levitation Model CE 152. It is a modified version of the paper [1]. The difference is that the identification and verification is done in open loop and constraints logic is added in the current paper. The author optimized and simplified dynamic model to a minimum to what is needed to characterize given system for the simulation and control design purposes. Only few open-loop experiments are needed to estimate the unknown parameters. Model quality is verified in open loop where the real and simulated data are compared. The model can serve as a simulation model for some…
A Saturation Model of the Synchronous Reluctance Motor and its Identification by Genetic Algorithms
2018
This paper proposes a complete saturation model of the Synchronous Reluctance Motor (Syn1231), accounting for both the self-saturation and cross-saturation effects. This model is based on an analytical relationship between the stator flux and current components, and is characterized by parameters presenting an interesting physical interpretation, differently from many other saturation model in the scientific literature. It proposes also an identification technique of such a model based on stand-still tests, without the need of locking the rotor. The proposed saturation model permits the complete description of the magnetic behaviour of the machine with 8 parameters, fewer than those require…