73,362 research outputs found
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
One of the challenges in modeling cognitive events from electroencephalogram
(EEG) data is finding representations that are invariant to inter- and
intra-subject differences, as well as to inherent noise associated with such
data. Herein, we propose a novel approach for learning such representations
from multi-channel EEG time-series, and demonstrate its advantages in the
context of mental load classification task. First, we transform EEG activities
into a sequence of topology-preserving multi-spectral images, as opposed to
standard EEG analysis techniques that ignore such spatial information. Next, we
train a deep recurrent-convolutional network inspired by state-of-the-art video
classification to learn robust representations from the sequence of images. The
proposed approach is designed to preserve the spatial, spectral, and temporal
structure of EEG which leads to finding features that are less sensitive to
variations and distortions within each dimension. Empirical evaluation on the
cognitive load classification task demonstrated significant improvements in
classification accuracy over current state-of-the-art approaches in this field.Comment: To be published as a conference paper at ICLR 201
Steered mixture-of-experts for light field images and video : representation and coding
Research in light field (LF) processing has heavily increased over the last decade. This is largely driven by the desire to achieve the same level of immersion and navigational freedom for camera-captured scenes as it is currently available for CGI content. Standardization organizations such as MPEG and JPEG continue to follow conventional coding paradigms in which viewpoints are discretely represented on 2-D regular grids. These grids are then further decorrelated through hybrid DPCM/transform techniques. However, these 2-D regular grids are less suited for high-dimensional data, such as LFs. We propose a novel coding framework for higher-dimensional image modalities, called Steered Mixture-of-Experts (SMoE). Coherent areas in the higher-dimensional space are represented by single higher-dimensional entities, called kernels. These kernels hold spatially localized information about light rays at any angle arriving at a certain region. The global model consists thus of a set of kernels which define a continuous approximation of the underlying plenoptic function. We introduce the theory of SMoE and illustrate its application for 2-D images, 4-D LF images, and 5-D LF video. We also propose an efficient coding strategy to convert the model parameters into a bitstream. Even without provisions for high-frequency information, the proposed method performs comparable to the state of the art for low-to-mid range bitrates with respect to subjective visual quality of 4-D LF images. In case of 5-D LF video, we observe superior decorrelation and coding performance with coding gains of a factor of 4x in bitrate for the same quality. At least equally important is the fact that our method inherently has desired functionality for LF rendering which is lacking in other state-of-the-art techniques: (1) full zero-delay random access, (2) light-weight pixel-parallel view reconstruction, and (3) intrinsic view interpolation and super-resolution
Shape and Texture Combined Face Recognition for Detection of Forged ID Documents
This paper proposes a face recognition system that can be used to effectively match a face image scanned from an identity (ID) doc-ument against the face image stored in the biometric chip of such a document. The purpose of this specific face recognition algorithm is to aid the automatic detection of forged ID documents where the photography printed on the documentâs surface has been altered or replaced. The proposed algorithm uses a novel combination of texture and shape features together with sub-space representation techniques. In addition, the robustness of the proposed algorithm when dealing with more general face recognition tasks has been proven with the Good, the Bad & the Ugly (GBU) dataset, one of the most challenging datasets containing frontal faces. The proposed algorithm has been complement-ed with a novel method that adopts two operating points to enhance the reliability of the algorithmâs final verification decision.Final Accepted Versio
A robust braille recognition system
Braille is the most effective means of written communication between
visually-impaired and sighted people. This paper describes a new system
that recognizes Braille characters in scanned Braille document pages. Unlike
most other approaches, an inexpensive flatbed scanner is used and the system
requires minimal interaction with the user. A unique feature of this system is
the use of context at different levels (from the pre-processing of the image
through to the post-processing of the recognition results) to enhance robustness
and, consequently, recognition results. Braille dots composing characters are
identified on both single and double-sided documents of average quality with
over 99% accuracy, while Braille characters are also correctly recognised in
over 99% of documents of average quality (in both single and double-sided
documents)
Village Economies and the Structure of Extended Family Networks
This paper documents how the structure of extended family networks in rural Mexico relates to the poverty and inequality of the village of residence. Using the Hispanic naming convention, we construct within-village extended family networks in 504 poor rural villages. Family networks are larger (both in the number of members and as a share of the village population) and out-migration is lower the poorer and the less unequal the village of residence. Our results are consistent with the extended family being a source of informal insurance to its members.extended family network, migration, village inequality, village marginality
Corporate Taxes, Profit Shifting and the Location of Intangibles within Multinational Firms
Intangible assets are one major source of profit shifting opportunities due to a highly intransparent transfer pricing process. Our paper argues that multinational enterprises (MNEs) optimize their profit shifting strategy by locating shiftingârelevant intangible property at affiliates with a low statutory corporate tax rate. Using panel data for European MNEs and controlling for unobserved timeâconstant heterogeneity between affiliates, we find that the lower a subsidiaryâs tax rate relative to other affiliates of the multinational group the higher is its level of intangible asset investment. This effect is statistically and economically significant, even after controlling for subsidiary size and accounting for a dynamic intangible investment pattern
LRMM: Learning to Recommend with Missing Modalities
Multimodal learning has shown promising performance in content-based
recommendation due to the auxiliary user and item information of multiple
modalities such as text and images. However, the problem of incomplete and
missing modality is rarely explored and most existing methods fail in learning
a recommendation model with missing or corrupted modalities. In this paper, we
propose LRMM, a novel framework that mitigates not only the problem of missing
modalities but also more generally the cold-start problem of recommender
systems. We propose modality dropout (m-drop) and a multimodal sequential
autoencoder (m-auto) to learn multimodal representations for complementing and
imputing missing modalities. Extensive experiments on real-world Amazon data
show that LRMM achieves state-of-the-art performance on rating prediction
tasks. More importantly, LRMM is more robust to previous methods in alleviating
data-sparsity and the cold-start problem.Comment: 11 pages, EMNLP 201
Corporate Taxes and the Location of Intangible Assets Within Multinational Firms
Intangible assets, like patents and trademarks, are increasingly seen as the key to competitive success and as the drivers of corporate profit. Moreover, they constitute a major source of profit shifting opportunities in multinational enterprises (MNEs) due to a highly intransparent transfer pricing process. This paper argues that for both reasons, MNEs have an incentive to locate intangible property at affiliates with a relatively low corporate tax rate. Using panel data on European MNEs and controlling for unobserved time--constant heterogeneity between affiliates, we find that the lower a subsidiary's tax rate relative to other affiliates of the multinational group the higher is its level of intangible asset investment. This effect is statistically and economically significant, even after controlling for subsidiary size and accounting for a dynamic intangible investment pattern
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