pwc-archive/papers-with-abstracts huggingface dataset:
Primal Meaning Recommendation via On-line Encyclopedia
Authors: Zhiyuan Zhang, Wei Li, Jingjing Xu, Xu sun
Date: 2018-08-14 00:00:00

Polysemy is a very common phenomenon in modern languages. Under many
circumstances, there exists a primal meaning for the expression. We define the
primal meaning of an expression to be a frequently used sense of that
expression from which its other frequent senses can be deduced. Many of the new
appearing meanings of the expressions are either originated from a primal
meaning, or are merely literal references to the original expression, e.g.,
apple (fruit), Apple (Inc), and Apple (movie). When constructing a knowledge
base from on-line encyclopedia data, it would be more efficient to be aware of
the information about the importance of the senses. In this paper, we would
like to explore a way to automatically recommend the primal meaning of an
expression based on the textual descriptions of the multiple senses of an
expression from on-line encyclopedia websites. We propose a hybrid model that
captures both the pattern of the description and the relationship between
different descriptions with both weakly supervised and unsupervised models. The
experiment results show that our method yields a good result with a P@1
(precision) score of 83.3 per cent, and a MAP (mean average precision) of 90.5
per cent, surpassing the UMFS-WE baseline by a big margin (P@1 is 61.1 per cent
and MAP is 76.3 per cent).
Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction
Authors: Nikita Moriakov, Koen Michielsen, Jonas Adler, Ritse Mann, Ioannis Sechopoulos, Jonas Teuwen
Date: 2018-08-14 00:00:00
Tasks: Deep Learning

Digital breast tomosynthesis is rapidly replacing digital mammography as the
basic x-ray technique for evaluation of the breasts. However, the sparse
sampling and limited angular range gives rise to different artifacts, which
manufacturers try to solve in several ways. In this study we propose an
extension of the Learned Primal-Dual algorithm for digital breast
tomosynthesis. The Learned Primal-Dual algorithm is a deep neural network
consisting of several `reconstruction blocks', which take in raw sinogram data
as the initial input, perform a forward and a backward pass by taking
projections and back-projections, and use a convolutional neural network to
produce an intermediate reconstruction result which is then improved further by
the successive reconstruction block. We extend the architecture by providing
breast thickness measurements as a mask to the neural network and allow it to
learn how to use this thickness mask. We have trained the algorithm on digital
phantoms and the corresponding noise-free/noisy projections, and then tested
the algorithm on digital phantoms for varying level of noise. Reconstruction
performance of the algorithms was compared visually, using MSE loss and
Structural Similarity Index. Results indicate that the proposed algorithm
outperforms the baseline iterative reconstruction algorithm in terms of
reconstruction quality for both breast edges and internal structures and is
robust to noise.
Applying the Closed World Assumption to SUMO-based FOL Ontologies for Effective Commonsense Reasoning
Authors: Javier Álvez, Itziar Gonzalez-Dios, German Rigau
Date: 2018-08-14 00:00:00
Tasks: Translation

Most commonly, the Open World Assumption is adopted as a standard strategy for the design, construction and use of ontologies. This strategy limits the inferencing capabilities of any system because non-asserted statements (missing knowledge) could be assumed to be alternatively true or false. As we will demonstrate, this is especially the case of first-order logic (FOL) ontologies where non-asserted statements is nowadays one of the main obstacles to its practical application in automated commonsense reasoning tasks. In this paper, we investigate the application of the Closed World Assumption (CWA) to enable a better exploitation of FOL ontologies by using state-of-the-art automated theorem provers. To that end, we explore different CWA formulations for the structural knowledge encoded in a FOL translation of the SUMO ontology, discovering that almost 30 % of the structural knowledge is missing. We evaluate these formulations on a practical experimentation using a very large commonsense benchmark obtained from WordNet through its mapping to SUMO. The results show that the competency of the ontology improves more than 50 % when reasoning under the CWA. Thus, applying the CWA automatically to FOL ontologies reduces their ambiguity and more commonsense questions can be answered
Joint Ground and Aerial Package Delivery Services: A Stochastic Optimization Approach
Authors: Suttinee Sawadsitang, Dusit Niyato, Puay-Siew Tan, Ping Wang
Date: 2018-08-14 00:00:00
Tasks: Stochastic Optimization

Unmanned aerial vehicles (UAVs), also known as drones, have emerged as a
promising mode of fast, energy-efficient, and cost-effective package delivery.
A considerable number of works have studied different aspects of drone package
delivery service by a supplier, one of which is delivery planning. However,
existing works addressing the planning issues consider a simple case of perfect
delivery without service interruption, e.g., due to accident which is common
and realistic. Therefore, this paper introduces the joint ground and aerial
delivery service optimization and planning (GADOP) framework. The framework
explicitly incorporates uncertainty of drone package delivery, i.e., takeoff
and breakdown conditions. The GADOP framework aims to minimize the total
delivery cost given practical constraints, e.g., traveling distance limit.
Specifically, we formulate the GADOP framework as a three-stage stochastic
integer programming model. To deal with the high complexity issue of the
problem, a decomposition method is adopted. Then, the performance of the GADOP
framework is evaluated by using two data sets including Solomon benchmark suite
and the real data from one of the Singapore logistics companies. The
performance evaluation clearly shows that the GADOP framework can achieve
significantly lower total payment than that of the baseline methods which do
not take uncertainty into account.
Explaining Queries over Web Tables to Non-Experts
Authors: Jonathan Berant, Daniel Deutch, Amir Globerson, Tova Milo, Tomer Wolfson
Date: 2018-08-14 00:00:00
Tasks: Management, Translation

Designing a reliable natural language (NL) interface for querying tables has
been a longtime goal of researchers in both the data management and natural
language processing (NLP) communities. Such an interface receives as input an
NL question, translates it into a formal query, executes the query and returns
the results. Errors in the translation process are not uncommon, and users
typically struggle to understand whether their query has been mapped correctly.
We address this problem by explaining the obtained formal queries to non-expert
users. Two methods for query explanations are presented: the first translates
queries into NL, while the second method provides a graphic representation of
the query cell-based provenance (in its execution on a given table). Our
solution augments a state-of-the-art NL interface over web tables, enhancing it
in both its training and deployment phase. Experiments, including a user study
conducted on Amazon Mechanical Turk, show our solution to improve both the
correctness and reliability of an NL interface.
Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements
Authors: Abhinav Shukla, Harish Katti, Mohan Kankanhalli, Ramanathan Subramanian
Date: 2018-08-14 00:00:00

Emotion evoked by an advertisement plays a key role in influencing brand
recall and eventual consumer choices. Automatic ad affect recognition has
several useful applications. However, the use of content-based feature
representations does not give insights into how affect is modulated by aspects
such as the ad scene setting, salient object attributes and their interactions.
Neither do such approaches inform us on how humans prioritize visual
information for ad understanding. Our work addresses these lacunae by
decomposing video content into detected objects, coarse scene structure, object
statistics and actively attended objects identified via eye-gaze. We measure
the importance of each of these information channels by systematically
incorporating related information into ad affect prediction models. Contrary to
the popular notion that ad affect hinges on the narrative and the clever use of
linguistic and social cues, we find that actively attended objects and the
coarse scene structure better encode affective information as compared to
individual scene objects or conspicuous background elements.
Reconciling Irrational Human Behavior with AI based Decision Making: A Quantum Probabilistic Approach
Authors: Sagar Uprety, Dawei Song
Date: 2018-08-14 00:00:00
Tasks: Decision Making

There are many examples of human decision making which cannot be modeled by
classical probabilistic and logic models, on which the current AI systems are
based. Hence the need for a modeling framework which can enable intelligent
systems to detect and predict cognitive biases in human decisions to facilitate
better human-agent interaction. We give a few examples of irrational behavior
and use a generalized probabilistic model inspired by the mathematical
framework of Quantum Theory to model and explain such behavior.
Unsupervised learning of foreground object detection
Authors: Ioana Croitoru, Simion-Vlad Bogolin, Marius Leordeanu
Date: 2018-08-14 00:00:00
Tasks: Image Segmentation, Object, object-detection, Object Detection, Object Discovery, Object Discovery In Videos, Saliency Detection, Semantic Segmentation, Unsupervised Image Segmentation

Unsupervised learning poses one of the most difficult challenges in computer
vision today. The task has an immense practical value with many applications in
artificial intelligence and emerging technologies, as large quantities of
unlabeled videos can be collected at relatively low cost. In this paper, we
address the unsupervised learning problem in the context of detecting the main
foreground objects in single images. We train a student deep network to predict
the output of a teacher pathway that performs unsupervised object discovery in
videos or large image collections. Our approach is different from published
methods on unsupervised object discovery. We move the unsupervised learning
phase during training time, then at test time we apply the standard
feed-forward processing along the student pathway. This strategy has the
benefit of allowing increased generalization possibilities during training,
while remaining fast at testing. Our unsupervised learning algorithm can run
over several generations of student-teacher training. Thus, a group of student
networks trained in the first generation collectively create the teacher at the
next generation. In experiments our method achieves top results on three
current datasets for object discovery in video, unsupervised image segmentation
and saliency detection. At test time the proposed system is fast, being one to
two orders of magnitude faster than published unsupervised methods.
DeepNeuro: an open-source deep learning toolbox for neuroimaging
Authors: Andrew Beers, James Brown, Ken Chang, Katharina Hoebel, Elizabeth Gerstner, Bruce Rosen, Jayashree Kalpathy-Cramer
Date: 2018-08-14 00:00:00
Tasks: Deep Learning, Friction

Translating neural networks from theory to clinical practice has unique
challenges, specifically in the field of neuroimaging. In this paper, we
present DeepNeuro, a deep learning framework that is best-suited to putting
deep learning algorithms for neuroimaging in practical usage with a minimum of
friction. We show how this framework can be used to both design and train
neural network architectures, as well as modify state-of-the-art architectures
in a flexible and intuitive way. We display the pre- and postprocessing
functions common in the medical imaging community that DeepNeuro offers to
ensure consistent performance of networks across variable users, institutions,
and scanners. And we show how pipelines created in DeepNeuro can be concisely
packaged into shareable Docker containers and command-line interfaces using
DeepNeuro's pipeline resources.
NFFT meets Krylov methods: Fast matrix-vector products for the graph Laplacian of fully connected networks
Authors: Dominik Alfke, Daniel Potts, Martin Stoll, Toni Volkmer
Date: 2018-08-14 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

The graph Laplacian is a standard tool in data science, machine learning, and
image processing. The corresponding matrix inherits the complex structure of
the underlying network and is in certain applications densely populated. This
makes computations, in particular matrix-vector products, with the graph
Laplacian a hard task. A typical application is the computation of a number of
its eigenvalues and eigenvectors. Standard methods become infeasible as the
number of nodes in the graph is too large. We propose the use of the fast
summation based on the nonequispaced fast Fourier transform (NFFT) to perform
the dense matrix-vector product with the graph Laplacian fast without ever
forming the whole matrix. The enormous flexibility of the NFFT algorithm allows
us to embed the accelerated multiplication into Lanczos-based eigenvalues
routines or iterative linear system solvers and even consider other than the
standard Gaussian kernels. We illustrate the feasibility of our approach on a
number of test problems from image segmentation to semi-supervised learning
based on graph-based PDEs. In particular, we compare our approach with the
Nystr\"om method. Moreover, we present and test an enhanced, hybrid version of
the Nystr\"om method, which internally uses the NFFT.
Automatic Airway Segmentation in chest CT using Convolutional Neural Networks
Authors: A. Garcia-Uceda Juarez, H. A. W. M. Tiddens, M. de Bruijne
Date: 2018-08-14 00:00:00
Tasks: Computed Tomography (CT), Data Augmentation, Segmentation

Segmentation of the airway tree from chest computed tomography (CT) images is
critical for quantitative assessment of airway diseases including
bronchiectasis and chronic obstructive pulmonary disease (COPD). However,
obtaining an accurate segmentation of airways from CT scans is difficult due to
the high complexity of airway structures. Recently, deep convolutional neural
networks (CNNs) have become the state-of-the-art for many segmentation tasks,
and in particular the so-called Unet architecture for biomedical images.
However, its application to the segmentation of airways still remains a
challenging task. This work presents a simple but robust approach based on a 3D
Unet to perform segmentation of airways from chest CTs. The method is trained
on a dataset composed of 12 CTs, and tested on another 6 CTs. We evaluate the
influence of different loss functions and data augmentation techniques, and
reach an average dice coefficient of 0.8 between the ground-truth and our
automated segmentations.
Small Sample Learning in Big Data Era
Authors: Jun Shu, Zongben Xu, Deyu Meng
Date: 2018-08-14 00:00:00
Tasks: Small Data Image Classification

As a promising area in artificial intelligence, a new learning paradigm,
called Small Sample Learning (SSL), has been attracting prominent research
attention in the recent years. In this paper, we aim to present a survey to
comprehensively introduce the current techniques proposed on this topic.
Specifically, current SSL techniques can be mainly divided into two categories.
The first category of SSL approaches can be called "concept learning", which
emphasizes learning new concepts from only few related observations. The
purpose is mainly to simulate human learning behaviors like recognition,
generation, imagination, synthesis and analysis. The second category is called
"experience learning", which usually co-exists with the large sample learning
manner of conventional machine learning. This category mainly focuses on
learning with insufficient samples, and can also be called small data learning
in some literatures. More extensive surveys on both categories of SSL
techniques are introduced and some neuroscience evidences are provided to
clarify the rationality of the entire SSL regime, and the relationship with
human learning process. Some discussions on the main challenges and possible
future research directions along this line are also presented.
Learning A Shared Transform Model for Skull to Digital Face Image Matching
Authors: Maneet Singh, Shruti Nagpal, Richa Singh, Mayank Vatsa, Afzel Noore
Date: 2018-08-14 00:00:00

Human skull identification is an arduous task, traditionally requiring the
expertise of forensic artists and anthropologists. This paper is an effort to
automate the process of matching skull images to digital face images, thereby
establishing an identity of the skeletal remains. In order to achieve this, a
novel Shared Transform Model is proposed for learning discriminative
representations. The model learns robust features while reducing the
intra-class variations between skulls and digital face images. Such a model can
assist law enforcement agencies by speeding up the process of skull
identification, and reducing the manual load. Experimental evaluation performed
on two pre-defined protocols of the publicly available IdentifyMe dataset
demonstrates the efficacy of the proposed model.
Deep Retinex Decomposition for Low-Light Enhancement
Authors: Chen Wei, Wenjing Wang, Wenhan Yang, Jiaying Liu
Date: 2018-08-14 00:00:00
Tasks: Denoising, Image Enhancement, Low-Light Image Enhancement

Retinex model is an effective tool for low-light image enhancement. It
assumes that observed images can be decomposed into the reflectance and
illumination. Most existing Retinex-based methods have carefully designed
hand-crafted constraints and parameters for this highly ill-posed
decomposition, which may be limited by model capacity when applied in various
scenes. In this paper, we collect a LOw-Light dataset (LOL) containing
low/normal-light image pairs and propose a deep Retinex-Net learned on this
dataset, including a Decom-Net for decomposition and an Enhance-Net for
illumination adjustment. In the training process for Decom-Net, there is no
ground truth of decomposed reflectance and illumination. The network is learned
with only key constraints including the consistent reflectance shared by paired
low/normal-light images, and the smoothness of illumination. Based on the
decomposition, subsequent lightness enhancement is conducted on illumination by
an enhancement network called Enhance-Net, and for joint denoising there is a
denoising operation on reflectance. The Retinex-Net is end-to-end trainable, so
that the learned decomposition is by nature good for lightness adjustment.
Extensive experiments demonstrate that our method not only achieves visually
pleasing quality for low-light enhancement but also provides a good
representation of image decomposition.
Probabilistic Ensemble of Collaborative Filters
Authors: Zhiyu Min, Dahua Lin
Date: 2018-06-26 00:00:00
Tasks: Collaborative Filtering

Collaborative filtering is an important technique for recommendation. Whereas
it has been repeatedly shown to be effective in previous work, its performance
remains unsatisfactory in many real-world applications, especially those where
the items or users are highly diverse. In this paper, we explore an
ensemble-based framework to enhance the capability of a recommender in handling
diverse data. Specifically, we formulate a probabilistic model which integrates
the items, the users, as well as the associations between them into a
generative process. On top of this formulation, we further derive a progressive
algorithm to construct an ensemble of collaborative filters. In each iteration,
a new filter is derived from re-weighted entries and incorporated into the
ensemble. It is noteworthy that while the algorithmic procedure of our
algorithm is apparently similar to boosting, it is derived from an essentially
different formulation and thus differs in several key technical aspects. We
tested the proposed method on three large datasets, and observed substantial
improvement over the state of the art, including L2Boost, an effective method
based on boosting.
Moving Object Segmentation in Jittery Videos by Stabilizing Trajectories Modeled in Kendall's Shape Space
Authors: Geethu Miriam Jacob, Sukhendu Das
Date: 2018-08-14 00:00:00
Tasks: Clustering, Object, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

Moving Object Segmentation is a challenging task for jittery/wobbly videos.
For jittery videos, the non-smooth camera motion makes discrimination between
foreground objects and background layers hard to solve. While most recent works
for moving video object segmentation fail in this scenario, our method
generates an accurate segmentation of a single moving object. The proposed
method performs a sparse segmentation, where frame-wise labels are assigned
only to trajectory coordinates, followed by the pixel-wise labeling of frames.
The sparse segmentation involving stabilization and clustering of trajectories
in a 3-stage iterative process. At the 1st stage, the trajectories are
clustered using pairwise Procrustes distance as a cue for creating an affinity
matrix. The 2nd stage performs a block-wise Procrustes analysis of the
trajectories and estimates Frechet means (in Kendall's shape space) of the
clusters. The Frechet means represent the average trajectories of the motion
clusters. An optimization function has been formulated to stabilize the Frechet
means, yielding stabilized trajectories at the 3rd stage. The accuracy of the
motion clusters are iteratively refined, producing distinct groups of
stabilized trajectories. Next, the labels obtained from the sparse segmentation
are propagated for pixel-wise labeling of the frames, using a GraphCut based
energy formulation. Use of Procrustes analysis and energy minimization in
Kendall's shape space for moving object segmentation in jittery videos, is the
novelty of this work. Second contribution comes from experiments performed on a
dataset formed of 20 real-world natural jittery videos, with manually annotated
ground truth. Experiments are done with controlled levels of artificial jitter
on videos of SegTrack2 dataset. Qualitative and quantitative results indicate
the superiority of the proposed method.