3,633 research outputs found
Large Area Crop Inventory Experiment (LACIE). LACIE transition year plan for the direct estimation of wheat from LANDSAT imagery
There are no author-identified significant results in this report
A modularity based spectral method for simultaneous community and anti-community detection
In a graph or complex network, communities and anti-communities are node sets
whose modularity attains extremely large values, positive and negative,
respectively. We consider the simultaneous detection of communities and
anti-communities, by looking at spectral methods based on various matrix-based
definitions of the modularity of a vertex set. Invariant subspaces associated
to extreme eigenvalues of these matrices provide indications on the presence of
both kinds of modular structure in the network. The localization of the
relevant invariant subspaces can be estimated by looking at particular matrix
angles based on Frobenius inner products
Nonlinear non-extensive approach for identification of structured information
The problem of separating structured information representing phenomena of
differing natures is considered. A structure is assumed to be independent of
the others if can be represented in a complementary subspace. When the
concomitant subspaces are well separated the problem is readily solvable by a
linear technique. Otherwise, the linear approach fails to correctly
discriminate the required information. Hence, a non extensive approach is
proposed. The resulting nonlinear technique is shown to be suitable for dealing
with cases that cannot be tackled by the linear one.Comment: Physica A, in pres
Single-trial analysis of EEG during rapid visual discrimination: enabling cortically-coupled computer vision
We describe our work using linear discrimination of multi-channel electroencephalography
for single-trial detection of neural signatures of visual recognition events. We demonstrate
the approach as a methodology for relating neural variability to response variability, describing
studies for response accuracy and response latency during visual target detection.
We then show how the approach can be utilized to construct a novel type of brain-computer
interface, which we term cortically-coupled computer vision. In this application, a large
database of images is triaged using the detected neural signatures. We show how ‘corticaltriaging’
improves image search over a strictly behavioral response
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating
Unsupervised graph representation learning (UGRL) has drawn increasing
research attention and achieved promising results in several graph analytic
tasks. Relying on the homophily assumption, existing UGRL methods tend to
smooth the learned node representations along all edges, ignoring the existence
of heterophilic edges that connect nodes with distinct attributes. As a result,
current methods are hard to generalize to heterophilic graphs where dissimilar
nodes are widely connected, and also vulnerable to adversarial attacks. To
address this issue, we propose a novel unsupervised Graph Representation
learning method with Edge hEterophily discriminaTing (GREET) which learns
representations by discriminating and leveraging homophilic edges and
heterophilic edges. To distinguish two types of edges, we build an edge
discriminator that infers edge homophily/heterophily from feature and structure
information. We train the edge discriminator in an unsupervised way through
minimizing the crafted pivot-anchored ranking loss, with randomly sampled node
pairs acting as pivots. Node representations are learned through contrasting
the dual-channel encodings obtained from the discriminated homophilic and
heterophilic edges. With an effective interplaying scheme, edge discriminating
and representation learning can mutually boost each other during the training
phase. We conducted extensive experiments on 14 benchmark datasets and multiple
learning scenarios to demonstrate the superiority of GREET.Comment: 14 pages, 7 tables, 6 figures, accepted by AAAI 202
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