Cue validity and object-based attention

In a previous study, Egly, Driver, and Rafal (1994) observed both space- and object-based components of visual selective attention. However, the mechanisms underlying these two components and the relationship between them are not well understood. In the present research, with a similar paradigm, these issues were addressed by manipulating cue validity. Behavioral results indicated the presence of both space- and object-based components under high cue validity, similar to the results of Egly et al.'s study. In addition, under low cue validity, the space-based component was absent, whereas the object-based component was maintained. Further event-related potential results demonstrated an object-based effect at a sensory level over the posterior areas of brain, and a space-based effect over the anterior region. The present data suggest that the space- and object-based components reflect mainly voluntary and reflexive mechanisms, respectively

New Lepton Family Symmetry and Neutrino Tribimaximal Mixing

The newly proposed finite symmetry Sigma(81) is applied to the problem of neutrino tribimaximal mixing. The result is more satisfactory than those of previous models based on A_4 in that the use of auxiliary symmetries (or mechanisms) may be avoided. Deviations from the tribimaximal pattern are expected, but because of its basic structure, only tan^2 (theta_12) may differ significantly from 0.5 (say 0.45) with sin^2 (2 theta_23) remaining very close to one, and theta_13 very nearly zero.Comment: 8 pages, no figur

Zero-shot keyword spotting for visual speech recognition in-the-wild

Visual keyword spotting (KWS) is the problem of estimating whether a text query occurs in a given recording using only video information. This paper focuses on visual KWS for words unseen during training, a real-world, practical setting which so far has received no attention by the community. To this end, we devise an end-to-end architecture comprising (a) a state-of-the-art visual feature extractor based on spatiotemporal Residual Networks, (b) a grapheme-to-phoneme model based on sequence-to-sequence neural networks, and (c) a stack of recurrent neural networks which learn how to correlate visual features with the keyword representation. Different to prior works on KWS, which try to learn word representations merely from sequences of graphemes (i.e. letters), we propose the use of a grapheme-to-phoneme encoder-decoder model which learns how to map words to their pronunciation. We demonstrate that our system obtains very promising visual-only KWS results on the challenging LRS2 database, for keywords unseen during training. We also show that our system outperforms a baseline which addresses KWS via automatic speech recognition (ASR), while it drastically improves over other recently proposed ASR-free KWS methods.Comment: Accepted at ECCV-201

TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References

In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction. In the testing stage, TandemNet can make accurate image prediction with an optional report text input. It also interprets its prediction by producing attention on the image and text informative feature pieces, and further generating diagnostic report paragraphs. Based on a pathological bladder cancer images and their diagnostic reports (BCIDR) dataset, sufficient experiments demonstrate that our method effectively learns and integrates knowledge from multimodalities and obtains significantly improved performance than comparing baselines.Comment: MICCAI2017 Ora

Superpixel Convolutional Networks using Bilateral Inceptions

In this paper we propose a CNN architecture for semantic image segmentation. We introduce a new 'bilateral inception' module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image. The feature spaces for bilateral filtering and other parameters of the module are learned end-to-end using standard backpropagation techniques. The bilateral inception module addresses two issues that arise with general CNN segmentation architectures. First, this module propagates information between (super) pixels while respecting image edges, thus using the structured information of the problem for improved results. Second, the layer recovers a full resolution segmentation result from the lower resolution solution of a CNN. In the experiments, we modify several existing CNN architectures by inserting our inception module between the last CNN (1x1 convolution) layers. Empirical results on three different datasets show reliable improvements not only in comparison to the baseline networks, but also in comparison to several dense-pixel prediction techniques such as CRFs, while being competitive in time.Comment: European Conference on Computer Vision (ECCV), 201

Finite Symmetry of Leptonic Mass Matrices

We search for possible symmetries present in the leptonic mixing data from SU(3) subgroups of order up to 511. Theoretical results based on symmetry are compared with global fits of experimental data in a chi-squared analysis, yielding the following results. There is no longer a group that can produce all the mixing data without a free parameter, but a number of them can accommodate the first or the second column of the mixing matrix. The only group that fits the third column is $\Delta(150)$. It predicts $\sin^22\theta_{13}=0.11$ and $\sin^22\theta_{23}=0.94$, in good agreement with experimental results.Comment: Version to appear in Physical Review