Search results for "embedding"

Biased GraphWalks for RDF Graph Embeddings

2017

Knowledge Graphs have been recognized as a valuable source for background information in many data mining, information retrieval, natural language processing, and knowledge extraction tasks. However, obtaining a suitable feature vector representation from RDF graphs is a challenging task. In this paper, we extend the RDF2Vec approach, which leverages language modeling techniques for unsupervised feature extraction from sequences of entities. We generate sequences by exploiting local information from graph substructures, harvested by graph walks, and learn latent numerical representations of entities in RDF graphs. We extend the way we compute feature vector representations by comparing twel…

PCR testing for Treponema pallidum in paraffin-embedded skin biopsy specimens: test design and impact on the diagnosis of syphilis

2007

Background: Syphilis, a chronic infection caused by Treponema pallidum (T. pallidum), is a disease which is increasing in incidence, and thus more and more becoming a differential diagnosis in routine pathology. Aim: Since histological changes are not specific, we sought to develop a polymerase chain reaction (PCR)-based molecular assay for the detection of T. pallidum in formalin-fixed, paraffin-embedded tissues, and evaluate its diagnostic power, especially in comparison with other ancillary methods, i.e. immunohistochemistry and Dieterle staining. Methods: 36 skin biopsies with the clinical and /or serological diagnosis of syphilis were evaluated by morphology, immunohistochemistry and s…

A novel method for network intrusion detection based on nonlinear SNE and SVM

2017

In the case of network intrusion detection data, pre-processing techniques have been extensively used to enhance the accuracy of the model. An ideal intrusion detection system (IDS) is one that has appreciable detection capability overall the group of attacks. An open research problem of this area is the lower detection rate for less frequent attacks, which result from the curse of dimensionality and imbalanced class distribution of the benchmark datasets. This work attempts to minimise the effects of imbalanced class distribution by applying random under-sampling of the majority classes and SMOTE-based oversampling of minority classes. In order to alleviate the issue arising from the curse…

Word Embeddings for Wine Recommender Systems Using Vocabularies of Experts and Consumers

2018

International audience; This vision paper proposes an approach to use the most advanced word embeddings techniques to bridge the gap between the discourses of experts and non-experts and more specifically the terminologies used by the two communities. Word embeddings makes it possible to find equivalent terms between experts and non-experts, by approach the similarity between words or by revealing hidden semantic relations. Thus, these controlled vocabularies with these new semantic enrichments are exploited in a hybrid recommendation system incorporating content-based ontology and keyword-based ontology to obtain relevant wines recommendations regardless of the level of expertise of the en…

Dynamic 2- and 3-connectivity on planar graphs

1992

We study the problem of maintaining the 2-edge-, 2-vertex-, and 3-edge-connected components of a dynamic planar graph subject to edge deletions. The 2-edge-connected components can be maintained in a total of O(n log n) time under any sequence of at most O(n) deletions. This gives O(log n) amortized time per deletion. The 2-vertex- and 3-edge-connected components can be maintained in a total of O(n log2n) time. This gives O(log2n) amortized time per deletion. The space required by all our data structures is O(n).

Kernel-Based Inference of Functions Over Graphs

2018

Abstract The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced. A particularly interesting—and prevalent in several fields of study—problem is that of inferring a function defined over the nodes of a network. This work presents a versatile kernel-based framework for tackling this inference problem that naturally subsumes and generalizes the reconstruction approaches put forth recently for the signal processing by the community studying graphs. Both the static and the dynamic settings are considered along with effective modeling approaches for addressing real-world problems. The analytical discussion herein is complement…

Study and identification of new molecular descriptors, finalized to the development of Virtual Screening techniques through the use of deep neural ne…

2022

Vectors of Pairwise Item Preferences

2019

Neural embedding has been widely applied as an effective category of vectorization methods in real-world recommender systems. However, its exploration of users’ explicit feedback on items, to create good quality user and item vectors is still limited. Existing neural embedding methods only consider the items that are accessed by the users, but neglect the scenario when a user gives high or low rating to a particular item. In this paper, we propose Pref2Vec, a method to generate vector representations of pairwise item preferences, users and items, which can be directly utilized for machine learning tasks. Specifically, Pref2Vec considers users’ pairwise item preferences as elementary units. …

Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits.

2008

Abstract Reproducing kernel Hilbert spaces regression procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are discussed from a theoretical perspective. It is argued that a nonparametric treatment may be needed for capturing the multiple and complex interactions potentially arising in whole-genome models, i.e., those based on thousands of single-nucleotide polymorphism (SNP) markers. After a review of reproducing kernel Hilbert spaces regression, it is shown that the statistical specification admits a standard mixed-effects linear model representation, with smoothing parameters treated as variance components.…

Improved embedded molecular cluster model

2002

We demonstrate that boundary effects (i.e., displacements of the cluster boundary atoms from their lattice sites and the difference between effective charges of the perfect crystal atoms and those of the cluster atoms in the case of a cluster with no point defect in it) in an embedded molecular cluster (EMC) model can be radically reduced. A new embedding scheme is proposed. It includes search for the structural elements (SE) of which perfect crystal is composed, use of corresponding to these SE expression for the total energy, and application of the degree of localization of equations consistent with the wave functions of the cluster. To get equations for the cluster wave functions, the pr…