pwc-archive/papers-with-abstracts huggingface dataset:
DeepChrome: Deep-learning for predicting gene expression from histone modifications
Authors: Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi
Date: 2016-07-07 00:00:00
Tasks: Deep Learning

Motivation: Histone modifications are among the most important factors that
control gene regulation. Computational methods that predict gene expression
from histone modification signals are highly desirable for understanding their
combinatorial effects in gene regulation. This knowledge can help in developing
'epigenetic drugs' for diseases like cancer. Previous studies for quantifying
the relationship between histone modifications and gene expression levels
either failed to capture combinatorial effects or relied on multiple methods
that separate predictions and combinatorial analysis. This paper develops a
unified discriminative framework using a deep convolutional neural network to
classify gene expression using histone modification data as input. Our system,
called DeepChrome, allows automatic extraction of complex interactions among
important features. To simultaneously visualize the combinatorial interactions
among histone modifications, we propose a novel optimization-based technique
that generates feature pattern maps from the learnt deep model. This provides
an intuitive description of underlying epigenetic mechanisms that regulate
genes. Results: We show that DeepChrome outperforms state-of-the-art models
like Support Vector Machines and Random Forests for gene expression
classification task on 56 different cell-types from REMC database. The output
of our visualization technique not only validates the previous observations but
also allows novel insights about combinatorial interactions among histone
modification marks, some of which have recently been observed by experimental
studies.
A characterization of product-form exchangeable feature probability functions
Authors: Marco Battiston, Stefano Favaro, Daniel M. Roy, Yee Whye Teh
Date: 2016-07-07 00:00:00
Tasks: Form

We characterize the class of exchangeable feature allocations assigning
probability $V_{n,k}\prod_{l=1}^{k}W_{m_{l}}U_{n-m_{l}}$ to a feature
allocation of $n$ individuals, displaying $k$ features with counts
$(m_{1},\ldots,m_{k})$ for these features. Each element of this class is
parametrized by a countable matrix $V$ and two sequences $U$ and $W$ of
non-negative weights. Moreover, a consistency condition is imposed to guarantee
that the distribution for feature allocations of $n-1$ individuals is recovered
from that of $n$ individuals, when the last individual is integrated out. In
Theorem 1.1, we prove that the only members of this class satisfying the
consistency condition are mixtures of the Indian Buffet Process over its mass
parameter $\gamma$ and mixtures of the Beta--Bernoulli model over its
dimensionality parameter $N$. Hence, we provide a characterization of these two
models as the only, up to randomization of the parameters, consistent
exchangeable feature allocations having the required product form.
Top-push Video-based Person Re-identification
Authors: Jin-Jie You, An-Cong Wu, Xiang Li, Wei-Shi Zheng
Date: 2016-04-29 00:00:00
Proceeding: CVPR 2016 6
Tasks: Person Re-Identification, Video-Based Person Re-Identification

Most existing person re-identification (re-id) models focus on matching still
person images across disjoint camera views. Since only limited information can
be exploited from still images, it is hard (if not impossible) to overcome the
occlusion, pose and camera-view change, and lighting variation problems. In
comparison, video-based re-id methods can utilize extra space-time information,
which contains much more rich cues for matching to overcome the mentioned
problems. However, we find that when using video-based representation, some
inter-class difference can be much more obscure than the one when using
still-image based representation, because different people could not only have
similar appearance but also have similar motions and actions which are hard to
align. To solve this problem, we propose a top-push distance learning model
(TDL), in which we integrate a top-push constrain for matching video features
of persons. The top-push constraint enforces the optimization on top-rank
matching in re-id, so as to make the matching model more effective towards
selecting more discriminative features to distinguish different persons. Our
experiments show that the proposed video-based re-id framework outperforms the
state-of-the-art video-based re-id methods.
Mapping Data to Ontologies with Exceptions Using Answer Set Programming
Authors: Daniel P. Lupp, Evgenij Thorstensen
Date: 2016-07-07 00:00:00

In ontology-based data access, databases are connected to an ontology via
mappings from queries over the database to queries over the ontology. In this
paper, we consider mappings from relational databases to first-order
ontologies, and define an ASP-based framework for GLAV mappings with queries
over the ontology in the mapping rule bodies. We show that this type of
mappings can be used to express constraints and exceptions, as well as being a
powerful mechanism for succinctly representing OBDA mappings. We give an
algorithm for brave reasoning in this setting, and show that this problem has
either the same data complexity as ASP (NP- complete), or it is at least as
hard as the complexity of checking entailment for the ontology queries.
Furthermore, we show that for ontologies with UCQ-rewritable queries there
exists a natural reduction from mapping programs to \exists-ASP, an extension
of ASP with existential variables that itself admits a natural reduction to
ASP.
Tubelets: Unsupervised action proposals from spatiotemporal super-voxels
Authors: Mihir Jain, Jan van Gemert, Hervé Jégou, Patrick Bouthemy, Cees G. M. Snoek
Date: 2016-07-07 00:00:00
Tasks: Action Localization

This paper considers the problem of localizing actions in videos as a
sequences of bounding boxes. The objective is to generate action proposals that
are likely to include the action of interest, ideally achieving high recall
with few proposals. Our contributions are threefold. First, inspired by
selective search for object proposals, we introduce an approach to generate
action proposals from spatiotemporal super-voxels in an unsupervised manner, we
call them Tubelets. Second, along with the static features from individual
frames our approach advantageously exploits motion. We introduce independent
motion evidence as a feature to characterize how the action deviates from the
background and explicitly incorporate such motion information in various stages
of the proposal generation. Finally, we introduce spatiotemporal refinement of
Tubelets, for more precise localization of actions, and pruning to keep the
number of Tubelets limited. We demonstrate the suitability of our approach by
extensive experiments for action proposal quality and action localization on
three public datasets: UCF Sports, MSR-II and UCF101. For action proposal
quality, our unsupervised proposals beat all other existing approaches on the
three datasets. For action localization, we show top performance on both the
trimmed videos of UCF Sports and UCF101 as well as the untrimmed videos of
MSR-II.
A Maturity Model for Public Administration as Open Translation Data Providers
Authors: Núria Bel, Mikel L. Forcada, Asunción Gómez-Pérez
Date: 2016-07-07 00:00:00
Tasks: Machine Translation, Management, Translation

Any public administration that produces translation data can be a provider of
useful reusable data to meet its own translation needs and the ones of other
public organizations and private companies that work with texts of the same
domain. These data can also be crucial to produce domain-tuned Machine
Translation systems. The organization's management of the translation process,
the characteristics of the archives of the generated resources and of the
infrastructure available to support them determine the efficiency and the
effectiveness with which the materials produced can be converted into reusable
data. However, it is of utmost importance that the organizations themselves
first become aware of the goods they are producing and, second, adapt their
internal processes to become optimal providers. In this article, we propose a
Maturity Model to help these organizations to achieve it by identifying the
different stages of the management of translation data that determine the path
to the aforementioned goal.
Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge
Authors: Luciano Serafini, Artur d'Avila Garcez
Date: 2016-06-14 00:00:00
Tasks: Logical Reasoning, Tensor Networks

We propose Logic Tensor Networks: a uniform framework for integrating
automatic learning and reasoning. A logic formalism called Real Logic is
defined on a first-order language whereby formulas have truth-value in the
interval [0,1] and semantics defined concretely on the domain of real numbers.
Logical constants are interpreted as feature vectors of real numbers. Real
Logic promotes a well-founded integration of deductive reasoning on a
knowledge-base and efficient data-driven relational machine learning. We show
how Real Logic can be implemented in deep Tensor Neural Networks with the use
of Google's tensorflow primitives. The paper concludes with experiments
applying Logic Tensor Networks on a simple but representative example of
knowledge completion.
Dealing with a large number of classes -- Likelihood, Discrimination or Ranking?
Authors: David Barber, Aleksandar Botev
Date: 2016-06-22 00:00:00

We consider training probabilistic classifiers in the case of a large number
of classes. The number of classes is assumed too large to perform exact
normalisation over all classes. To account for this we consider a simple
approach that directly approximates the likelihood. We show that this simple
approach works well on toy problems and is competitive with recently introduced
alternative non-likelihood based approximations. Furthermore, we relate this
approach to a simple ranking objective. This leads us to suggest a specific
setting for the optimal threshold in the ranking objective.
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network
Authors: Xibin Song, Yuchao Dai, Xueying Qin
Date: 2016-07-07 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Depth image super-resolution is an extremely challenging task due to the
information loss in sub-sampling. Deep convolutional neural network have been
widely applied to color image super-resolution. Quite surprisingly, this
success has not been matched to depth super-resolution. This is mainly due to
the inherent difference between color and depth images. In this paper, we
bridge up the gap and extend the success of deep convolutional neural network
to depth super-resolution. The proposed deep depth super-resolution method
learns the mapping from a low-resolution depth image to a high resolution one
in an end-to-end style. Furthermore, to better regularize the learned depth
map, we propose to exploit the depth field statistics and the local correlation
between depth image and color image. These priors are integrated in an energy
minimization formulation, where the deep neural network learns the unary term,
the depth field statistics works as global model constraint and the color-depth
correlation is utilized to enforce the local structure in depth images.
Extensive experiments on various depth super-resolution benchmark datasets show
that our method outperforms the state-of-the-art depth image super-resolution
methods with a margin.
Stock trend prediction using news sentiment analysis
Authors: Joshi Kalyani, Prof. H. N. Bharathi, Prof. Rao Jyothi
Date: 2016-07-07 00:00:00
Tasks: Articles, General Classification, Prediction, Sentiment Analysis, Sentiment Classification, Stock Prediction, Stock Trend Prediction

Efficient Market Hypothesis is the popular theory about stock prediction.
With its failure much research has been carried in the area of prediction of
stocks. This project is about taking non quantifiable data such as financial
news articles about a company and predicting its future stock trend with news
sentiment classification. Assuming that news articles have impact on stock
market, this is an attempt to study relationship between news and stock trend.
To show this, we created three different classification models which depict
polarity of news articles being positive or negative. Observations show that RF
and SVM perform well in all types of testing. Na\"ive Bayes gives good result
but not compared to the other two. Experiments are conducted to evaluate
various aspects of the proposed model and encouraging results are obtained in
all of the experiments. The accuracy of the prediction model is more than 80%
and in comparison with news random labeling with 50% of accuracy; the model has
increased the accuracy by 30%.
Constitutional Precedent of Amicus Briefs
Authors: Allen Huang, Lars Roemheld
Date: 2016-06-15 00:00:00

We investigate shared language between U.S. Supreme Court majority opinions
and interest groups' corresponding amicus briefs. Specifically, we evaluate
whether language that originated in an amicus brief acquired legal precedent
status by being cited in the Court's opinion. Using plagiarism detection
software, automated querying of a large legal database, and manual analysis, we
establish seven instances where interest group amici were able to formulate
constitutional case law, setting binding legal precedent. We discuss several
such instances for their implications in the Supreme Court's creation of case
law.
Log-Euclidean Bag of Words for Human Action Recognition
Authors: Masoud Faraki, Maziar Palhang, Conrad Sanderson
Date: 2014-06-09 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

Representing videos by densely extracted local space-time features has
recently become a popular approach for analysing actions. In this paper, we
tackle the problem of categorising human actions by devising Bag of Words (BoW)
models based on covariance matrices of spatio-temporal features, with the
features formed from histograms of optical flow. Since covariance matrices form
a special type of Riemannian manifold, the space of Symmetric Positive Definite
(SPD) matrices, non-Euclidean geometry should be taken into account while
discriminating between covariance matrices. To this end, we propose to embed
SPD manifolds to Euclidean spaces via a diffeomorphism and extend the BoW
approach to its Riemannian version. The proposed BoW approach takes into
account the manifold geometry of SPD matrices during the generation of the
codebook and histograms. Experiments on challenging human action datasets show
that the proposed method obtains notable improvements in discrimination
accuracy, in comparison to several state-of-the-art methods.
Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images
Authors: Xianming Liu, Gene Cheung, Xiaolin Wu, Debin Zhao
Date: 2016-07-07 00:00:00
Tasks: Clustering, Image Reconstruction, Quantization

Given the prevalence of JPEG compressed images, optimizing image
reconstruction from the compressed format remains an important problem. Instead
of simply reconstructing a pixel block from the centers of indexed DCT
coefficient quantization bins (hard decoding), soft decoding reconstructs a
block by selecting appropriate coefficient values within the indexed bins with
the help of signal priors. The challenge thus lies in how to define suitable
priors and apply them effectively.
  In this paper, we combine three image priors---Laplacian prior for DCT
coefficients, sparsity prior and graph-signal smoothness prior for image
patches---to construct an efficient JPEG soft decoding algorithm. Specifically,
we first use the Laplacian prior to compute a minimum mean square error (MMSE)
initial solution for each code block. Next, we show that while the sparsity
prior can reduce block artifacts, limiting the size of the over-complete
dictionary (to lower computation) would lead to poor recovery of high DCT
frequencies. To alleviate this problem, we design a new graph-signal smoothness
prior (desired signal has mainly low graph frequencies) based on the left
eigenvectors of the random walk graph Laplacian matrix (LERaG). Compared to
previous graph-signal smoothness priors, LERaG has desirable image filtering
properties with low computation overhead. We demonstrate how LERaG can
facilitate recovery of high DCT frequencies of a piecewise smooth (PWS) signal
via an interpretation of low graph frequency components as relaxed solutions to
normalized cut in spectral clustering. Finally, we construct a soft decoding
algorithm using the three signal priors with appropriate prior weights.
Experimental results show that our proposal outperforms state-of-the-art soft
decoding algorithms in both objective and subjective evaluations noticeably.
Deep Perceptual Mapping for Cross-Modal Face Recognition
Authors: M. Saquib Sarfraz, Rainer Stiefelhagen
Date: 2016-01-20 00:00:00
Tasks: Face Recognition

Cross modal face matching between the thermal and visible spectrum is a much
desired capability for night-time surveillance and security applications. Due
to a very large modality gap, thermal-to-visible face recognition is one of the
most challenging face matching problem. In this paper, we present an approach
to bridge this modality gap by a significant margin. Our approach captures the
highly non-linear relationship between the two modalities by using a deep
neural network. Our model attempts to learn a non-linear mapping from visible
to thermal spectrum while preserving the identity information. We show
substantive performance improvement on three difficult thermal-visible face
datasets. The presented approach improves the state-of-the-art by more than
10\% on UND-X1 dataset and by more than 15-30\% on NVESD dataset in terms of
Rank-1 identification. Our method bridges the drop in performance due to the
modality gap by more than 40\%.
Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising
Authors: Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic, Fabrizio Silvestri, Ricardo Baeza-Yates, Andrew Feng, Erik Ordentlich, Lee Yang, Gavin Owens
Date: 2016-07-07 00:00:00

Sponsored search represents a major source of revenue for web search engines.
This popular advertising model brings a unique possibility for advertisers to
target users' immediate intent communicated through a search query, usually by
displaying their ads alongside organic search results for queries deemed
relevant to their products or services. However, due to a large number of
unique queries it is challenging for advertisers to identify all such relevant
queries. For this reason search engines often provide a service of advanced
matching, which automatically finds additional relevant queries for advertisers
to bid on. We present a novel advanced matching approach based on the idea of
semantic embeddings of queries and ads. The embeddings were learned using a
large data set of user search sessions, consisting of search queries, clicked
ads and search links, while utilizing contextual information such as dwell time
and skipped ads. To address the large-scale nature of our problem, both in
terms of data and vocabulary size, we propose a novel distributed algorithm for
training of the embeddings. Finally, we present an approach for overcoming a
cold-start problem associated with new ads and queries. We report results of
editorial evaluation and online tests on actual search traffic. The results
show that our approach significantly outperforms baselines in terms of
relevance, coverage, and incremental revenue. Lastly, we open-source learned
query embeddings to be used by researchers in computational advertising and
related fields.
Neural Name Translation Improves Neural Machine Translation
Authors: Xiao-Qing Li, Jiajun Zhang, Cheng-qing Zong
Date: 2016-07-07 00:00:00
Tasks: Machine Translation, NMT, Translation, Word Alignment

In order to control computational complexity, neural machine translation
(NMT) systems convert all rare words outside the vocabulary into a single unk
symbol. Previous solution (Luong et al., 2015) resorts to use multiple numbered
unks to learn the correspondence between source and target rare words. However,
testing words unseen in the training corpus cannot be handled by this method.
And it also suffers from the noisy word alignment. In this paper, we focus on a
major type of rare words -- named entity (NE), and propose to translate them
with character level sequence to sequence model. The NE translation model is
further used to derive high quality NE alignment in the bilingual training
corpus. With the integration of NE translation and alignment modules, our NMT
system is able to surpass the baseline system by 2.9 BLEU points on the Chinese
to English task.
Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images
Authors: Hao Chen, Yefeng Zheng, Jin-Hyeong Park, Pheng-Ann Heng, S. Kevin Zhou
Date: 2016-07-07 00:00:00
Tasks: Segmentation, Transfer Learning

Accurate detection and segmentation of anatomical structures from ultrasound
images are crucial for clinical diagnosis and biometric measurements. Although
ultrasound imaging has been widely used with superiorities such as low cost and
portability, the fuzzy border definition and existence of abounding artifacts
pose great challenges for automatically detecting and segmenting the complex
anatomical structures. In this paper, we propose a multi-domain regularized
deep learning method to address this challenging problem. By leveraging the
transfer learning from cross domains, the feature representations are
effectively enhanced. The results are further improved by the iterative
refinement. Moreover, our method is quite efficient by taking advantage of a
fully convolutional network, which is formulated as an end-to-end learning
framework of detection and segmentation. Extensive experimental results on a
large-scale database corroborated that our method achieved a superior detection
and segmentation accuracy, outperforming other methods by a significant margin
and demonstrating competitive capability even compared to human performance.
Cost-Optimal Algorithms for Planning with Procedural Control Knowledge
Authors: Vikas Shivashankar, Ron Alford, Mark Roberts, David W. Aha
Date: 2016-07-06 00:00:00

There is an impressive body of work on developing heuristics and other
reasoning algorithms to guide search in optimal and anytime planning algorithms
for classical planning. However, very little effort has been directed towards
developing analogous techniques to guide search towards high-quality solutions
in hierarchical planning formalisms like HTN planning, which allows using
additional domain-specific procedural control knowledge. In lieu of such
techniques, this control knowledge often needs to provide the necessary search
guidance to the planning algorithm, which imposes a substantial burden on the
domain author and can yield brittle or error-prone domain models. We address
this gap by extending recent work on a new hierarchical goal-based planning
formalism called Hierarchical Goal Network (HGN) Planning to develop the
Hierarchically-Optimal Goal Decomposition Planner (HOpGDP), an HGN planning
algorithm that computes hierarchically-optimal plans. HOpGDP is guided by
$h_{HL}$, a new HGN planning heuristic that extends existing admissible
landmark-based heuristics from classical planning to compute admissible cost
estimates for HGN planning problems. Our experimental evaluation across three
benchmark planning domains shows that HOpGDP compares favorably to both optimal
classical planners due to its ability to use domain-specific procedural
knowledge, and a blind-search version of HOpGDP due to the search guidance
provided by $h_{HL}$.