pwc-archive/papers-with-abstracts huggingface dataset:
Constructing a Word Similarity Graph from Vector based Word Representation for Named Entity Recognition
Authors: Miguel Feria, Juan Paolo Balbin, Francis Michael Bautista
Date: 2018-07-09 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Word Similarity

In this paper, we discuss a method for identifying a seed word that would
best represent a class of named entities in a graphical representation of words
and their similarities. Word networks, or word graphs, are representations of
vectorized text where nodes are the words encountered in a corpus, and the
weighted edges incident on the nodes represent how similar the words are to
each other. We intend to build a bilingual word graph and identify seed words
through community analysis that would be best used to segment a graph according
to its named entities, therefore providing an unsupervised way of tagging named
entities for a bilingual language base.
XNOR Neural Engine: a Hardware Accelerator IP for 21.6 fJ/op Binary Neural Network Inference
Authors: Francesco Conti, Pasquale Davide Schiavone, Luca Benini
Date: 2018-07-09 00:00:00

Binary Neural Networks (BNNs) are promising to deliver accuracy comparable to
conventional deep neural networks at a fraction of the cost in terms of memory
and energy. In this paper, we introduce the XNOR Neural Engine (XNE), a fully
digital configurable hardware accelerator IP for BNNs, integrated within a
microcontroller unit (MCU) equipped with an autonomous I/O subsystem and hybrid
SRAM / standard cell memory. The XNE is able to fully compute convolutional and
dense layers in autonomy or in cooperation with the core in the MCU to realize
more complex behaviors. We show post-synthesis results in 65nm and 22nm
technology for the XNE IP and post-layout results in 22nm for the full MCU
indicating that this system can drop the energy cost per binary operation to
21.6fJ per operation at 0.4V, and at the same time is flexible and performant
enough to execute state-of-the-art BNN topologies such as ResNet-34 in less
than 2.2mJ per frame at 8.9 fps.
A Sequence-to-Sequence Model for Semantic Role Labeling
Authors: Angel Daza, Anette Frank
Date: 2018-07-09 00:00:00
Proceeding: WS 2018 7
Tasks: Benchmarking, Semantic Role Labeling

We explore a novel approach for Semantic Role Labeling (SRL) by casting it as
a sequence-to-sequence process. We employ an attention-based model enriched
with a copying mechanism to ensure faithful regeneration of the input sequence,
while enabling interleaved generation of argument role labels. Here, we apply
this model in a monolingual setting, performing PropBank SRL on English
language data. The constrained sequence generation set-up enforced with the
copying mechanism allows us to analyze the performance and special properties
of the model on manually labeled data and benchmarking against state-of-the-art
sequence labeling models. We show that our model is able to solve the SRL
argument labeling task on English data, yet further structural decoding
constraints will need to be added to make the model truly competitive. Our work
represents a first step towards more advanced, generative SRL labeling setups.
Towards Enhancing Lexical Resource and Using Sense-annotations of OntoSenseNet for Sentiment Analysis
Authors: Sreekavitha Parupalli, Vijjini Anvesh Rao, Radhika Mamidi
Date: 2018-07-09 00:00:00
Proceeding: COLING 2018 8
Tasks: Sentiment Analysis, Word Sense Disambiguation

This paper illustrates the interface of the tool we developed for crowd
sourcing and we explain the annotation procedure in detail. Our tool is named
as 'Parupalli Padajaalam' which means web of words by Parupalli. The aim of
this tool is to populate the OntoSenseNet, sentiment polarity annotated Telugu
resource. Recent works have shown the importance of word-level annotations on
sentiment analysis. With this as basis, we aim to analyze the importance of
sense-annotations obtained from OntoSenseNet in performing the task of
sentiment analysis. We explain the fea- tures extracted from OntoSenseNet
(Telugu). Furthermore we compute and explain the adverbial class distribution
of verbs in OntoSenseNet. This task is known to aid in disambiguating
word-senses which helps in enhancing the performance of word-sense
disambiguation (WSD) task(s).
Learning Functions in Large Networks requires Modularity and produces Multi-Agent Dynamics
Authors: C. -H. Huck Yang, Rise Ooi, Tom Hiscock, Victor Eguiluz, Jesper Tegnér
Date: 2018-07-09 00:00:00

Networks are abundant in biological systems. Small sized over-represented
network motifs have been discovered, and it has been suggested that these
constitute functional building blocks. We ask whether larger dynamical network
motifs exist in biological networks, thus contributing to the higher-order
organization of a network. To end this, we introduce a gradient descent machine
learning (ML) approach and genetic algorithms to learn larger functional motifs
in contrast to an (unfeasible) exhaustive search. We use the French Flag (FF)
and Switch functional motif as case studies motivated from biology. While our
algorithm successfully learns large functional motifs, we identify a threshold
size of approximately 20 nodes beyond which learning breaks down. Therefore we
investigate the stability of the motifs. We find that the size of the real
negative eigenvalues of the Jacobian decreases with increasing system size,
thus conferring instability. Finally, without imposing learning an input-output
for all the components of the network, we observe that unconstrained middle
components of the network still learn the desired function, a form of
homogeneous team learning. We conclude that the size limitation of
learnability, most likely due to stability constraints, impose a definite
requirement for modularity in networked systems while enabling team learning
within unconstrained parts of the module. Thus, the observation that community
structures and modularity are abundant in biological networks could be
accounted for by a computational compositional network structure.
Decreasing the size of the Restricted Boltzmann machine
Authors: Yohei Saito, Takuya Kato
Date: 2018-07-09 00:00:00

We propose a method to decrease the number of hidden units of the restricted
Boltzmann machine while avoiding decrease of the performance measured by the
Kullback-Leibler divergence. Then, we demonstrate our algorithm by using
numerical simulations.
Polarimetric Convolutional Network for PolSAR Image Classification
Authors: Xu Liu, Licheng Jiao, Xu Tang, Qigong Sun, Dan Zhang
Date: 2018-07-09 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

The approaches for analyzing the polarimetric scattering matrix of
polarimetric synthetic aperture radar (PolSAR) data have always been the focus
of PolSAR image classification. Generally, the polarization coherent matrix and
the covariance matrix obtained by the polarimetric scattering matrix only show
a limited number of polarimetric information. In order to solve this problem,
we propose a sparse scattering coding way to deal with polarimetric scattering
matrix and obtain a close complete feature. This encoding mode can also
maintain polarimetric information of scattering matrix completely. At the same
time, in view of this encoding way, we design a corresponding classification
algorithm based on convolution network to combine this feature. Based on sparse
scattering coding and convolution neural network, the polarimetric
convolutional network is proposed to classify PolSAR images by making full use
of polarimetric information. We perform the experiments on the PolSAR images
acquired by AIRSAR and RADARSAT-2 to verify the proposed method. The
experimental results demonstrate that the proposed method get better results
and has huge potential for PolSAR data classification. Source code for sparse
scattering coding is available at
https://github.com/liuxuvip/Polarimetric-Scattering-Coding.
Universal Word Segmentation: Implementation and Interpretation
Authors: Yan Shao, Christian Hardmeier, Joakim Nivre
Date: 2018-07-09 00:00:00
Proceeding: TACL 2018 1
Tasks: Segmentation

Word segmentation is a low-level NLP task that is non-trivial for a
considerable number of languages. In this paper, we present a sequence tagging
framework and apply it to word segmentation for a wide range of languages with
different writing systems and typological characteristics. Additionally, we
investigate the correlations between various typological factors and word
segmentation accuracy. The experimental results indicate that segmentation
accuracy is positively related to word boundary markers and negatively to the
number of unique non-segmental terms. Based on the analysis, we design a small
set of language-specific settings and extensively evaluate the segmentation
system on the Universal Dependencies datasets. Our model obtains
state-of-the-art accuracies on all the UD languages. It performs substantially
better on languages that are non-trivial to segment, such as Chinese, Japanese,
Arabic and Hebrew, when compared to previous work.
Jointly learning relevant subgraph patterns and nonlinear models of their indicators
Authors: Ryo Shirakawa, Yusei Yokoyama, Fumiya Okazaki, Ichigaku Takigawa
Date: 2018-07-09 00:00:00
Tasks: Computational chemistry, regression

Classification and regression in which the inputs are graphs of arbitrary
size and shape have been paid attention in various fields such as computational
chemistry and bioinformatics. Subgraph indicators are often used as the most
fundamental features, but the number of possible subgraph patterns are
intractably large due to the combinatorial explosion. We propose a novel
efficient algorithm to jointly learn relevant subgraph patterns and nonlinear
models of their indicators. Previous methods for such joint learning of
subgraph features and models are based on search for single best subgraph
features with specific pruning and boosting procedures of adding their
indicators one by one, which result in linear models of subgraph indicators. In
contrast, the proposed approach is based on directly learning regression trees
for graph inputs using a newly derived bound of the total sum of squares for
data partitions by a given subgraph feature, and thus can learn nonlinear
models through standard gradient boosting. An illustrative example we call the
Graph-XOR problem to consider nonlinearity, numerical experiments with real
datasets, and scalability comparisons to naive approaches using explicit
pattern enumeration are also presented.
Flow Network Tracking for Spatiotemporal and Periodic Point Matching: Applied to Cardiac Motion Analysis
Authors: Nripesh Parajuli, Allen Lu, Kevinminh Ta, John C. Stendahl, Nabil Boutagy, Imran Alkhalil, Melissa Eberle, Geng-Shi Jeng, Maria Zontak, Matthew ODonnell, Albert J. Sinusas, James S. Duncan
Date: 2018-07-09 00:00:00

The accurate quantification of left ventricular (LV) deformation/strain shows
significant promise for quantitatively assessing cardiac function for use in
diagnosis and therapy planning (Jasaityte et al., 2013). However, accurate
estimation of the displacement of myocardial tissue and hence LV strain has
been challenging due to a variety of issues, including those related to
deriving tracking tokens from images and following tissue locations over the
entire cardiac cycle. In this work, we propose a point matching scheme where
correspondences are modeled as flow through a graphical network. Myocardial
surface points are set up as nodes in the network and edges define neighborhood
relationships temporally. The novelty lies in the constraints that are imposed
on the matching scheme, which render the correspondences one-to-one through the
entire cardiac cycle, and not just two consecutive frames. The constraints also
encourage motion to be cyclic, which is an important characteristic of LV
motion. We validate our method by applying it to the estimation of quantitative
LV displacement and strain estimation using 8 synthetic and 8 open-chested
canine 4D echocardiographic image sequences, the latter with sonomicrometric
crystals implanted on the LV wall. We were able to achieve excellent tracking
accuracy on the synthetic dataset and observed a good correlation with
crystal-based strains on the in-vivo data.
Human Activity Recognition in RGB-D Videos by Dynamic Images
Authors: Snehasis Mukherjee, Leburu Anvitha, T. Mohana Lahari
Date: 2018-07-09 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Human Activity Recognition in RGB-D videos has been an active research topic
during the last decade. However, no efforts have been found in the literature,
for recognizing human activity in RGB-D videos where several performers are
performing simultaneously. In this paper we introduce such a challenging
dataset with several performers performing the activities. We present a novel
method for recognizing human activities in such videos. The proposed method
aims in capturing the motion information of the whole video by producing a
dynamic image corresponding to the input video. We use two parallel ResNext-101
to produce the dynamic images for the RGB video and depth video separately. The
dynamic images contain only the motion information and hence, the unnecessary
background information are eliminated. We send the two dynamic images extracted
from the RGB and Depth videos respectively, through a fully connected layer of
neural networks. The proposed dynamic image reduces the complexity of the
recognition process by extracting a sparse matrix from a video. However, the
proposed system maintains the required motion information for recognizing the
activity. The proposed method has been tested on the MSR Action 3D dataset and
has shown comparable performances with respect to the state-of-the-art. We also
apply the proposed method on our own dataset, where the proposed method
outperforms the state-of-the-art approaches.
Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma
Authors: Zhuotun Zhu, Yingda Xia, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille
Date: 2018-07-09 00:00:00
Tasks: General Classification, Segmentation, Sensitivity, Specificity

We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans. Our idea is named multi-scale segmentation-for-classification, which classifies volumes by checking if at least a sufficient number of voxels is segmented as tumors, by which we can provide radiologists with tumor locations. In order to deal with tumors with different scales, we train and test our volumetric segmentation networks with multi-scale inputs in a coarse-to-fine flowchart. A post-processing module is used to filter out outliers and reduce false alarms. We collect a new dataset containing 439 CT scans, in which 136 cases were diagnosed with PDAC and 303 cases are normal, which is the largest set for PDAC tumors to the best of our knowledge. To offer the best trade-off between sensitivity and specificity, our proposed framework reports a sensitivity of 94.1% at a specificity of 98.5%, which demonstrates the potential to make a clinical impact.
PARN: Pyramidal Affine Regression Networks for Dense Semantic Correspondence
Authors: Sangryul Jeon, Seungryong Kim, Dongbo Min, Kwanghoon Sohn
Date: 2018-07-09 00:00:00
Proceeding: ECCV 2018 9
Tasks: regression, Semantic correspondence

This paper presents a deep architecture for dense semantic correspondence,
called pyramidal affine regression networks (PARN), that estimates
locally-varying affine transformation fields across images. To deal with
intra-class appearance and shape variations that commonly exist among different
instances within the same object category, we leverage a pyramidal model where
affine transformation fields are progressively estimated in a coarse-to-fine
manner so that the smoothness constraint is naturally imposed within deep
networks. PARN estimates residual affine transformations at each level and
composes them to estimate final affine transformations. Furthermore, to
overcome the limitations of insufficient training data for semantic
correspondence, we propose a novel weakly-supervised training scheme that
generates progressive supervisions by leveraging a correspondence consistency
across image pairs. Our method is fully learnable in an end-to-end manner and
does not require quantizing infinite continuous affine transformation fields.
To the best of our knowledge, it is the first work that attempts to estimate
dense affine transformation fields in a coarse-to-fine manner within deep
networks. Experimental results demonstrate that PARN outperforms the
state-of-the-art methods for dense semantic correspondence on various
benchmarks.
Step-by-step Erasion, One-by-one Collection: A Weakly Supervised Temporal Action Detector
Authors: Jia-Xing Zhong, Nannan Li, Weijie Kong, Tao Zhang, Thomas H. Li, Ge Li
Date: 2018-07-09 00:00:00
Tasks: Action Detection, Temporal Localization

Weakly supervised temporal action detection is a Herculean task in
understanding untrimmed videos, since no supervisory signal except the
video-level category label is available on training data. Under the supervision
of category labels, weakly supervised detectors are usually built upon
classifiers. However, there is an inherent contradiction between classifier and
detector; i.e., a classifier in pursuit of high classification performance
prefers top-level discriminative video clips that are extremely fragmentary,
whereas a detector is obliged to discover the whole action instance without
missing any relevant snippet. To reconcile this contradiction, we train a
detector by driving a series of classifiers to find new actionness clips
progressively, via step-by-step erasion from a complete video. During the test
phase, all we need to do is to collect detection results from the one-by-one
trained classifiers at various erasing steps. To assist in the collection
process, a fully connected conditional random field is established to refine
the temporal localization outputs. We evaluate our approach on two prevailing
datasets, THUMOS'14 and ActivityNet. The experiments show that our detector
advances state-of-the-art weakly supervised temporal action detection results,
and even compares with quite a few strongly supervised methods.
Zero-shot Domain Adaptation without Domain Semantic Descriptors
Authors: Atsutoshi Kumagai, Tomoharu Iwata
Date: 2018-07-09 00:00:00
Tasks: Domain Adaptation

We propose a method to infer domain-specific models such as classifiers for
unseen domains, from which no data are given in the training phase, without
domain semantic descriptors. When training and test distributions are
different, standard supervised learning methods perform poorly. Zero-shot
domain adaptation attempts to alleviate this problem by inferring models that
generalize well to unseen domains by using training data in multiple source
domains. Existing methods use observed semantic descriptors characterizing
domains such as time information to infer the domain-specific models for the
unseen domains. However, it cannot always be assumed that such metadata can be
used in real-world applications. The proposed method can infer appropriate
domain-specific models without any semantic descriptors by introducing the
concept of latent domain vectors, which are latent representations for the
domains and are used for inferring the models. The latent domain vector for the
unseen domain is inferred from the set of the feature vectors in the
corresponding domain, which is given in the testing phase. The domain-specific
models consist of two components: the first is for extracting a representation
of a feature vector to be predicted, and the second is for inferring model
parameters given the latent domain vector. The posterior distributions of the
latent domain vectors and the domain-specific models are parametrized by neural
networks, and are optimized by maximizing the variational lower bound using
stochastic gradient descent. The effectiveness of the proposed method was
demonstrated through experiments using one regression and two classification
tasks.
Vehicle Image Generation Going Well with The Surroundings
Authors: Jeesoo Kim, Jangho Kim, Jaeyoung Yoo, Daesik Kim, Nojun Kwak
Date: 2018-07-09 00:00:00
Tasks: Colorization, Image Generation, Image Restoration, Image Segmentation, Object, object-detection, Object Detection, Semantic Segmentation, Style Transfer

Since the generative neural networks have made a breakthrough in the image generation problem, lots of researches on their applications have been studied such as image restoration, style transfer and image completion. However, there has been few research generating objects in uncontrolled real-world environments. In this paper, we propose a novel approach for vehicle image generation in real-world scenes. Using a subnetwork based on a precedent work of image completion, our model makes the shape of an object. Details of objects are trained by an additional colorization and refinement subnetwork, resulting in a better quality of generated objects. Unlike many other works, our method does not require any segmentation layout but still makes a plausible vehicle in the image. We evaluate our method by using images from Berkeley Deep Drive (BDD) and Cityscape datasets, which are widely used for object detection and image segmentation problems. The adequacy of the generated images by the proposed method has also been evaluated using a widely utilized object detection algorithm and the FID score.
Domain2Vec: Deep Domain Generalization
Authors: Aniket Anand Deshmukh, Ankit Bansal, Akash Rastogi
Date: 2018-07-09 00:00:00
Tasks: Domain Generalization, image-classification, Image Classification

We address the problem of domain generalization where a decision function is
learned from the data of several related domains, and the goal is to apply it
on an unseen domain successfully. It is assumed that there is plenty of labeled
data available in source domains (also called as training domain), but no
labeled data is available for the unseen domain (also called a target domain or
test domain). We propose a novel neural network architecture, Domain2Vec (D2V)
that learns domain-specific embedding and then uses this embedding to
generalize the learning across related domains. The proposed algorithm, D2V
extends the idea of distribution regression and kernelized domain
generalization to the neural networks setting. We propose a neural network
architecture to learn domain-specific embedding and then use this embedding
along with the data point specific features to label it. We show the
effectiveness of the architecture by accurately estimating domain to domain
similarity. We evaluate our algorithm against standard domain generalization
datasets for image classification and outperform other state of the art
algorithms.
Attention to Refine through Multi-Scales for Semantic Segmentation
Authors: Shiqi Yang, Gang Peng
Date: 2018-07-09 00:00:00
Tasks: Semantic Segmentation

This paper proposes a novel attention model for semantic segmentation, which
aggregates multi-scale and context features to refine prediction. Specifically,
the skeleton convolutional neural network framework takes in multiple different
scales inputs, by which means the CNN can get representations in different
scales. The proposed attention model will handle the features from different
scale streams respectively and integrate them. Then location attention branch
of the model learns to softly weight the multi-scale features at each pixel
location. Moreover, we add an recalibrating branch, parallel to where location
attention comes out, to recalibrate the score map per class. We achieve quite
competitive results on PASCAL VOC 2012 and ADE20K datasets, which surpass
baseline and related works.