310 research outputs found
SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension
We present a novel neural architecture for the Argument Reasoning
Comprehension task of SemEval 2018. It is a simple neural network consisting of
three parts, collectively judging whether the logic built on a set of given
sentences (a claim, reason, and warrant) is plausible or not. The model
utilizes contextualized word vectors pre-trained on large machine translation
(MT) datasets as a form of transfer learning, which can help to mitigate the
lack of training data. Quantitative analysis shows that simply leveraging LSTMs
trained on MT datasets outperforms several baselines and non-transferred
models, achieving accuracies of about 70% on the development set and about 60%
on the test set.Comment: SemEval 201
Effects of Perinatal Exposure to Cocaine on Sexually Differentiated Gonadotropin Regulation in Rats
Slopes of smooth curves on Fano manifolds
Ross and Thomas introduced the concept of slope stability to study
K-stability, which has conjectural relation with the existence of constant
scalar curvature K\"ahler metric. This paper presents a study of slope
stability of Fano manifolds of dimension with respect to smooth
curves. The question turns out to be easy for curves of genus and the
interest lies in the case of smooth rational curves. Our main result classifies
completely the cases when a polarized Fano manifold is not slope
stable with respect to a smooth curve. Our result also states that a Fano
threefold with Picard number 1 is slope stable with respect to every smooth
curve unless is the projective space.Comment: 13 pages, Theorems in the original version were modified. This paper
will be published in the Bulletin of the London Mathematical Societ
Weighted inhomogeneous regularization for inverse problems with indirect and incomplete measurement data
Regularization promotes well-posedness in solving an inverse problem with
incomplete measurement data. The regularization term is typically designed
based on a priori characterization of the unknown signal, such as sparsity or
smoothness. The standard inhomogeneous regularization incorporates a spatially
changing exponent of the standard norm-based regularization to
recover a signal whose characteristic varies spatially. This study proposes a
weighted inhomogeneous regularization that extends the standard inhomogeneous
regularization through new exponent design and weighting using spatially
varying weights. The new exponent design avoids misclassification when
different characteristics stay close to each other. The weights handle another
issue when the region of one characteristic is too small to be recovered
effectively by the norm-based regularization even after identified
correctly. A suite of numerical tests shows the efficacy of the proposed
weighted inhomogeneous regularization, including synthetic image experiments
and real sea ice recovery from its incomplete wave measurements
Learning a Manifold-Constrained Map between Image Sets: Applications to Matching and Pose Estimation
This paper proposes a method for matching two sets of images given a small number of training examples by exploiting the underlying structure of the image manifolds. A nonlinear map from one manifold to another is constructed by combining linear maps locally defined on the tangent spaces of the manifolds. This construction imposes strong constraints on the choice of the maps, and makes possible good generalization of correspondences between all of the image sets. This map is flexible enough to approximate an arbitrary diffeomorphism between manifolds and can serve many purposes for applications. The underlying algorithm is a non-iterative efficient procedure whose complexity mainly depends on the number of matched training examples and the dimensionality of the manifold, and not on the number of samples nor on the dimensionality of the images. Several experiments were performed to demonstrate the potential of our method in image analysis and pose estimation. The first example demonstrates how images from a rotating camera can be mapped to the underlying pose manifold. Second, computer generated images from articulating toy figures are matched using the underlying 4 dimensional manifold to generate image-driven animations. Finally, two sets of actual lip images during speech are matched by their appearance manifold. In all these cases, our algorithm is able to obtain reasonable matches between thousands of large-dimensional images, with a minimum of computation
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