Search results for "Embedding"
showing 10 items of 175 documents
EMBEDDING CONTEXT IN INVESTIGATIONS OF AFFECTIVE VARIABILITY: AGE DIFFERENCES IN AFFECT-HEALTH LINKS
2017
Context plays a potentially important role in explaining variability in affective experiences and yet, has often been overlooked in this line of research. The current study used data from a lifespan sample of 398 German participants ranging between 12–88 years of age (M = 40, SD = 20). Participants completed computer assisted personal interviews regarding health and well-being measures, as well as experience sampling assessments of daily affective experiences and events (e.g., uplifts). Three indices of positive affect (PA) were created: mean PA, PA reactivity to uplifts, and PA variability. In general, greater mean PA and lower PA reactivity and variability were associated with better heal…
Predicting the Short-Term Exchange Rate Between United State Dollar and Czech Koruna Using Hilbert-Huang Transform and Fuzzy Logic
2017
In this paper, the combination of the Hilbert-Huang Transform, fuzzy logic and an embedding theorem is described to predict the short-term exchange rate from United States dollar to Czech Koruna. By Using the Hilbert-Huang Transform as an adaptive filter, the proposed method decreases the embedding dimension space from five (original samples) to four (de-noising samples). This dimension space provides the number of inputs to the fuzzy rule base system, which causes the number of rules, the time for training and the inference process to decrease. Experimental results indicated that this method achieves higher accuracy prediction than the direct use of original data.
Nonlinear analysis of continuous ECG during sleep I. Reconstruction.
2000
In recent years evidence has accumulated that ECG signals are of a nonlinear nature. It has been recognized that strictly periodic cardiac rhythms are not accompanied by healthy conditions but, on the contrary, by pathological states. Therefore, the application of methods from nonlinear system theory for the analysis of ECG signals has gained increasing interest. Crucial for the application of nonlinear methods is the reconstruction (embedding) of the time series in a phase space with appropriate dimension. In this study continuous ECG signals of 12 healthy subjects recorded during different sleep stages were analysed. Proper embedding dimension was determined by application of two techniqu…
15-prostaglandin dehydrogenase expression alone or in combination with ACSM1 defines a subgroup of the apocrine molecular subtype of breast carcinoma.
2008
Established histopathological criteria divide invasive breast carcinomas into defined groups. Ductal of no specific type and lobular are the two major subtypes accounting for around 75 and 15% of all cases, respectively. The remaining 10% include rarer types such as tubular, cribriform, mucinous, papillary, medullary, metaplastic, and apocrine breast carcinomas. Molecular profiling technologies, on the other hand, subdivide breast tumors into five subtypes, basal-like, luminal A, luminal B, normal breast tissue-like, and ERBB2-positive, that have different prognostic characteristics. An additional subclass termed "molecular apocrine" has recently been described, but these lesions did not ex…
Rank two aCM bundles on the del Pezzo fourfold of degree 6 and its general hyperplane section
2018
International audience; In the present paper we completely classify locally free sheaves of rank 2 with vanishing intermediate cohomology modules on the image of the Segre embedding $\mathbb{P}^2$ x $\mathbb{P}^2 \subseteq \mathbb{P}^8$ and its general hyperplane sections.Such a classification extends similar already known results regarding del Pezzo varieties with Picard numbers 1 and 3 and dimension at least 3.
Quasi-Projective Varieties
2000
We have developed the theory of affine and projective varieties separately. We now introduce the concept of a quasi-projective variety, a term that encompasses both cases. More than just a convenience, the notion of a quasi-projective variety will eventually allow us to think of an algebraic variety as an intrinsically defined geometric object, free from any particular embedding in affine or projective space.
On the derived category of the Cayley plane II
2014
We find a full strongly exceptional collection for the Cayley plane OP2, the simplest rational homogeneous space of the exceptional group E6. This collection, closely related to the one given by the second author in [J. Algebra, 330:177-187, 2011], consists of 27 vector bundles which are homogeneous for the group E6, and is a Lefschetz collection with respect to the minimal equivariant embedding of OP2.
On the -hypercentre of a finite group
2008
The main objective of this paper is to study and describe the hypercentre of a finite group associated with saturated formations, in terms of some subgroup embedding properties related to permutability. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
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).
Sparse Manifold Clustering and Embedding to discriminate gene expression profiles of glioblastoma and meningioma tumors.
2013
Sparse Manifold Clustering and Embedding (SMCE) algorithm has been recently proposed for simultaneous clustering and dimensionality reduction of data on nonlinear manifolds using sparse representation techniques. In this work, SMCE algorithm is applied to the differential discrimination of Glioblastoma and Meningioma Tumors by means of their Gene Expression Profiles. Our purpose was to evaluate the robustness of this nonlinear manifold to classify gene expression profiles, characterized by the high-dimensionality of their representations and the low discrimination power of most of the genes. For this objective, we used SMCE to reduce the dimensionality of a preprocessed dataset of 35 single…