We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing ...penalties based on the /spl lscr//sub 1/-norm. A number of recent theoretical results on sparsifying properties of /spl lscr//sub 1/ penalties justify this choice. Explicitly enforcing the sparsity of the representation is motivated by a desire to obtain a sharp estimate of the spatial spectrum that exhibits super-resolution. We propose to use the singular value decomposition (SVD) of the data matrix to summarize multiple time or frequency samples. Our formulation leads to an optimization problem, which we solve efficiently in a second-order cone (SOC) programming framework by an interior point implementation. We propose a grid refinement method to mitigate the effects of limiting estimates to a grid of spatial locations and introduce an automatic selection criterion for the regularization parameter involved in our approach. We demonstrate the effectiveness of the method on simulated data by plots of spatial spectra and by comparing the estimator variance to the Crame/spl acute/r-Rao bound (CRB). We observe that our approach has a number of advantages over other source localization techniques, including increased resolution, improved robustness to noise, limitations in data quantity, and correlation of the sources, as well as not requiring an accurate initialization.
This research is concerned with studying the representations of the event in the drawings of the ancient civilizations of the world, and the research consists of two axes, the axis of the theoretical ...framework, which included (the research problem, its aim, its limits, and the definition of its terminology). The research aims to reveal how the event pattern was formulated by the artist on the surface of his visual achievement, and the limits of the search were spatial in the ancient civilizations of Iraq, Egypt, Greece and Rome, but the limits of the temporal research could not be determined because they were before birth, and objectively:representations of the event in the civilizations of the ancient world This axis also included four topics The first: the event in the arts of ancient Iraq, the second: the event in the arts of ancient Egypt, the third: the event in Greek art, and the fourth: the event in Roman art, ending with the indicators produced by the theoretical framework, while the second applied axis was represented by the research procedures, namely (the research community The research concluded with the results and the most important sources that the researcher relied on.
FELLA: an R package to enrich metabolomics data Picart-Armada, Sergio; Fernández-Albert, Francesc; Vinaixa, Maria ...
BMC bioinformatics,
12/2018, Volume:
19, Issue:
1
Journal Article, Publication
Peer reviewed
Open access
Pathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in ...metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects.
We introduce FELLA, an R package to perform a network-based enrichment of a list of affected metabolites. FELLA builds a hierarchical representation of an organism biochemistry from the Kyoto Encyclopedia of Genes and Genomes (KEGG), containing pathways, modules, enzymes, reactions and metabolites. In addition to providing a list of pathways, FELLA reports intermediate entities (modules, enzymes, reactions) that link the input metabolites to them. This sheds light on pathway cross talk and potential enzymes or metabolites as targets for the condition under study. FELLA has been applied to six public datasets -three from Homo sapiens, two from Danio rerio and one from Mus musculus- and has reproduced findings from the original studies and from independent literature.
The R package FELLA offers an innovative enrichment concept starting from a list of metabolites, based on a knowledge graph representation of the KEGG database that focuses on interpretability. Besides reporting a list of pathways, FELLA suggests intermediate entities that are of interest per se. Its usefulness has been shown at several molecular levels on six public datasets, including human and animal models. The user can run the enrichment analysis through a simple interactive graphical interface or programmatically. FELLA is publicly available in Bioconductor under the GPL-3 license.
Visible-infrared person re-identification (RGB-IR ReID) has now attracted increasing attention due to its surveillance applications under low-light environments. However, the large intra-class ...variations between different domains are still a challenging issue in the field of computer vision. To address the above issue, we propose a novel adversarial Decoupling and Modality-invariant Representation learning (DMiR) method to explore potential spectrum-invariant yet identity-discriminative representations for cross-modality pedestrians. Our model consists of three key components, including Domain-related Representation Disentanglement (DrRD), Modality-invariant Discriminative Representation (MiDR) and Representation Orthogonal Decorrelation (ROD). First, two subnets named Identity-Net and Domain-Net are designed to extract identity-related features and domain-related features, respectively. Given this two-stream structure, the DrRD is introduced to achieve adversarial decoupling against domain-specific features via a min-max disentanglement process. Specifically, the classification objective function on Domain-Net is minimized to extract spectrum-specific information while maximizing it to reduce domain-specific information. Second, in Identity-Net, we introduce MiDR to enhance intra-class compactness and reduce domain variations by exploring positive and negative pair variations, semantic-wise differences, and pair-wise semantic variations. Finally, the correlation between the two decomposed features, i.e., identity-related features and domain-related features, may lead to the introduction of modal information in identity representations, and vice versa. Therefore, we present the ROD constraint to make the two decomposed features unrelated to each other, which can more effectively separate the two-component features and enhance feature representations. Practically, we construct ROD at the feature-level and parameter-level, and finally select feature-level ROD as the decorrelation strategy because of its superior decorrelation performance. The whole scheme leads to disentangling spectrum-dependent information, as well as purifying identity information. Extensive experiments are carried out on two mainstream RGB-IR ReID datasets, and the results demonstrate the effectiveness of our method.
Abstract Drops from a muddy puddle produce a distinct pattern on the side doors of a car driving through the puddle. This pattern was studied to determine the speed of the car. We can conceive at ...least two different models for pattern formation. We can decide in favor of one of the models by using different representations of the data. The activity is based on a rich context-based problem of projectile motion and offers epistemological insight into how physics research works by forming and testing hypotheses and using alternative representations.
We address single image super-resolution using a statistical prediction model based on sparse representations of low- and high-resolution image patches. The suggested model allows us to avoid any ...invariance assumption, which is a common practice in sparsity-based approaches treating this task. Prediction of high resolution patches is obtained via MMSE estimation and the resulting scheme has the useful interpretation of a feedforward neural network. To further enhance performance, we suggest data clustering and cascading several levels of the basic algorithm. We suggest a training scheme for the resulting network and demonstrate the capabilities of our algorithm, showing its advantages over existing methods based on a low- and high-resolution dictionary pair, in terms of computational complexity, numerical criteria, and visual appearance. The suggested approach offers a desirable compromise between low computational complexity and reconstruction quality, when comparing it with state-of-the-art methods for single image super-resolution.
Learning graph representations is a fundamental task aimed at capturing various properties of graphs in vector space. The most recent methods learn such representations for static networks. However, ...real-world networks evolve over time and have varying dynamics. Capturing such evolution is key to predicting the properties of unseen networks. To understand how the network dynamics affect the prediction performance, we propose an embedding approach which learns the structure of evolution in dynamic graphs and can predict unseen links with higher precision. Our model, dyngraph2vec, learns the temporal transitions in the network using a deep architecture composed of dense and recurrent layers. We motivate the need for capturing dynamics for the prediction on a toy dataset created using stochastic block models. We then demonstrate the efficacy of dyngraph2vec over existing state-of-the-art methods on two real-world datasets. We observe that learning dynamics can improve the quality of embedding and yield better performance in link prediction.
Learning representations of data is an important problem in statistics and machine learning. While the origin of learning representations can be traced back to factor analysis and multidimensional ...scaling in statistics, it has become a central theme in deep learning with important applications in computer vision and computational neuroscience. In this article, we review recent advances in learning representations from a statistical perspective. In particular, we review the following two themes: (
a
) unsupervised learning of vector representations and (
b
) learning of both vector and matrix representations.
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