pwc-archive/papers-with-abstracts huggingface dataset:
Scout-It: Interior tomography using modified scout acquisition
Authors: Kriti Sen Sharma
Date: 2016-08-14 00:00:00

Global scout views have been previously used to reduce interior
reconstruction artifacts in high-resolution micro-CT and C-arm systems. However
these methods cannot be directly used in the all-important domain of clinical
CT. This is because when the CT scan is truncated, the scout views are also
truncated. However many cases of truncation in clinical CT involve partial
truncation, where the anterio-posterior (AP) scout is truncated, but the
medio-lateral (ML) scout is non-truncated. In this paper, we show that in such
cases of partially truncated CT scans, a modified configuration may be used to
acquire non-truncated AP scout view, and ultimately allow for highly accurate
interior reconstruction.
SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation
Authors: Ting Liu, Miaomiao Zhang, Mehran Javanmardi, Nisha Ramesh, Tolga Tasdizen
Date: 2016-08-14 00:00:00
Tasks: Electron Microscopy Image Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

Region-based methods have proven necessary for improving segmentation
accuracy of neuronal structures in electron microscopy (EM) images. Most
region-based segmentation methods use a scoring function to determine region
merging. Such functions are usually learned with supervised algorithms that
demand considerable ground truth data, which are costly to collect. We propose
a semi-supervised approach that reduces this demand. Based on a merge tree
structure, we develop a differentiable unsupervised loss term that enforces
consistent predictions from the learned function. We then propose a Bayesian
model that combines the supervised and the unsupervised information for
probabilistic learning. The experimental results on three EM data sets
demonstrate that by using a subset of only 3% to 7% of the entire ground truth
data, our approach consistently performs close to the state-of-the-art
supervised method with the full labeled data set, and significantly outperforms
the supervised method with the same labeled subset.
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
Authors: Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez, Ermin Hodzic, Raunak Shrestha, Avishek Saha, Hua Ouyang, Dawei Yin, Hiroshi Mamitsuka, Cenk Sahinalp, Predrag Radivojac, Filippo Menczer, Yi Chang
Date: 2016-08-14 00:00:00
Tasks: Cloud Computing, Dimensionality Reduction, feature selection, Vocal Bursts Intensity Prediction

Machine learning methods are used to discover complex nonlinear relationships
in biological and medical data. However, sophisticated learning models are
computationally unfeasible for data with millions of features. Here we
introduce the first feature selection method for nonlinear learning problems
that can scale up to large, ultra-high dimensional biological data. More
specifically, we scale up the novel Hilbert-Schmidt Independence Criterion
Lasso (HSIC Lasso) to handle millions of features with tens of thousand
samples. The proposed method is guaranteed to find an optimal subset of
maximally predictive features with minimal redundancy, yielding higher
predictive power and improved interpretability. Its effectiveness is
demonstrated through applications to classify phenotypes based on module
expression in human prostate cancer patients and to detect enzymes among
protein structures. We achieve high accuracy with as few as 20 out of one
million features --- a dimensionality reduction of 99.998%. Our algorithm can
be implemented on commodity cloud computing platforms. The dramatic reduction
of features may lead to the ubiquitous deployment of sophisticated prediction
models in mobile health care applications.
Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees
Authors: Kyle Simek, Ravishankar Palanivelu, Kobus Barnard
Date: 2016-08-14 00:00:00
Tasks: 3D Reconstruction, Gaussian Processes

We propose a robust method for estimating dynamic 3D curvilinear branching
structure from monocular images. While 3D reconstruction from images has been
widely studied, estimating thin structure has received less attention. This
problem becomes more challenging in the presence of camera error, scene motion,
and a constraint that curves are attached in a branching structure. We propose
a new general-purpose prior, a branching Gaussian processes (BGP), that models
spatial smoothness and temporal dynamics of curves while enforcing attachment
between them. We apply this prior to fit 3D trees directly to image data, using
an efficient scheme for approximate inference based on expectation propagation.
The BGP prior's Gaussian form allows us to approximately marginalize over 3D
trees with a given model structure, enabling principled comparison between tree
models with varying complexity. We test our approach on a novel multi-view
dataset depicting plants with known 3D structures and topologies undergoing
small nonrigid motion. Our method outperforms a state-of-the-art 3D
reconstruction method designed for non-moving thin structure. We evaluate under
several common measures, and we propose a new measure for reconstructions of
branching multi-part 3D scenes under motion.
Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?
Authors: Arturo Deza, Miguel P. Eckstein
Date: 2016-08-14 00:00:00
Proceeding: NeurIPS 2016 12

Previous studies have proposed image-based clutter measures that correlate
with human search times and/or eye movements. However, most models do not take
into account the fact that the effects of clutter interact with the foveated
nature of the human visual system: visual clutter further from the fovea has an
increasing detrimental influence on perception. Here, we introduce a new
foveated clutter model to predict the detrimental effects in target search
utilizing a forced fixation search task. We use Feature Congestion (Rosenholtz
et al.) as our non foveated clutter model, and we stack a peripheral
architecture on top of Feature Congestion for our foveated model. We introduce
the Peripheral Integration Feature Congestion (PIFC) coefficient, as a
fundamental ingredient of our model that modulates clutter as a non-linear gain
contingent on eccentricity. We finally show that Foveated Feature Congestion
(FFC) clutter scores r(44) = -0.82 correlate better with target detection (hit
rate) than regular Feature Congestion r(44) = -0.19 in forced fixation search.
Thus, our model allows us to enrich clutter perception research by computing
fixation specific clutter maps. A toolbox for creating peripheral
architectures: Piranhas: Peripheral Architectures for Natural, Hybrid and
Artificial Systems will be made available.
An approach to dealing with missing values in heterogeneous data using k-nearest neighbors
Authors: Davi E. N. Frossard, Igor O. Nunes, Renato A. Krohling
Date: 2016-08-13 00:00:00
Tasks: Decision Making, Imputation, Missing Values

Techniques such as clusterization, neural networks and decision making
usually rely on algorithms that are not well suited to deal with missing
values. However, real world data frequently contains such cases. The simplest
solution is to either substitute them by a best guess value or completely
disregard the missing values. Unfortunately, both approaches can lead to biased
results. In this paper, we propose a technique for dealing with missing values
in heterogeneous data using imputation based on the k-nearest neighbors
algorithm. It can handle real (which we refer to as crisp henceforward),
interval and fuzzy data. The effectiveness of the algorithm is tested on
several datasets and the numerical results are promising.
Ego2Top: Matching Viewers in Egocentric and Top-view Videos
Authors: Shervin Ardeshir, Ali Borji
Date: 2016-07-24 00:00:00
Tasks: Graph Matching

Egocentric cameras are becoming increasingly popular and provide us with
large amounts of videos, captured from the first person perspective. At the
same time, surveillance cameras and drones offer an abundance of visual
information, often captured from top-view. Although these two sources of
information have been separately studied in the past, they have not been
collectively studied and related. Having a set of egocentric cameras and a
top-view camera capturing the same area, we propose a framework to identify the
egocentric viewers in the top-view video. We utilize two types of features for
our assignment procedure. Unary features encode what a viewer (seen from
top-view or recording an egocentric video) visually experiences over time.
Pairwise features encode the relationship between the visual content of a pair
of viewers. Modeling each view (egocentric or top) by a graph, the assignment
process is formulated as spectral graph matching. Evaluating our method over a
dataset of 50 top-view and 188 egocentric videos taken in different scenarios
demonstrates the efficiency of the proposed approach in assigning egocentric
viewers to identities present in top-view camera. We also study the effect of
different parameters such as the number of egocentric viewers and visual
features.
Incremental Method for Spectral Clustering of Increasing Orders
Authors: Pin-Yu Chen, Baichuan Zhang, Mohammad Al Hasan, Alfred O. Hero
Date: 2015-12-23 00:00:00
Tasks: Clustering, Community Detection

The smallest eigenvalues and the associated eigenvectors (i.e., eigenpairs)
of a graph Laplacian matrix have been widely used for spectral clustering and
community detection. However, in real-life applications the number of clusters
or communities (say, $K$) is generally unknown a-priori. Consequently, the
majority of the existing methods either choose $K$ heuristically or they repeat
the clustering method with different choices of $K$ and accept the best
clustering result. The first option, more often, yields suboptimal result,
while the second option is computationally expensive. In this work, we propose
an incremental method for constructing the eigenspectrum of the graph Laplacian
matrix. This method leverages the eigenstructure of graph Laplacian matrix to
obtain the $K$-th eigenpairs of the Laplacian matrix given a collection of all
the $K-1$ smallest eigenpairs. Our proposed method adapts the Laplacian matrix
such that the batch eigenvalue decomposition problem transforms into an
efficient sequential leading eigenpair computation problem. As a practical
application, we consider user-guided spectral clustering. Specifically, we
demonstrate that users can utilize the proposed incremental method for
effective eigenpair computation and determining the desired number of clusters
based on multiple clustering metrics.
Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning
Authors: Ekaterina Vylomova, Laura Rimell, Trevor Cohn, Timothy Baldwin
Date: 2015-09-05 00:00:00
Proceeding: ACL 2016 8
Tasks: Clustering, Relation, Word Embeddings

Recent work on word embeddings has shown that simple vector subtraction over
pre-trained embeddings is surprisingly effective at capturing different lexical
relations, despite lacking explicit supervision. Prior work has evaluated this
intriguing result using a word analogy prediction formulation and hand-selected
relations, but the generality of the finding over a broader range of lexical
relation types and different learning settings has not been evaluated. In this
paper, we carry out such an evaluation in two learning settings: (1) spectral
clustering to induce word relations, and (2) supervised learning to classify
vector differences into relation types. We find that word embeddings capture a
surprising amount of information, and that, under suitable supervised training,
vector subtraction generalises well to a broad range of relations, including
over unseen lexical items.
An Analysis of Lemmatization on Topic Models of Morphologically Rich Language
Authors: Chandler May, Ryan Cotterell, Benjamin Van Durme
Date: 2016-08-13 00:00:00
Tasks: Articles, Lemmatization, Topic Models

Topic models are typically represented by top-$m$ word lists for human interpretation. The corpus is often pre-processed with lemmatization (or stemming) so that those representations are not undermined by a proliferation of words with similar meanings, but there is little public work on the effects of that pre-processing. Recent work studied the effect of stemming on topic models of English texts and found no supporting evidence for the practice. We study the effect of lemmatization on topic models of Russian Wikipedia articles, finding in one configuration that it significantly improves interpretability according to a word intrusion metric. We conclude that lemmatization may benefit topic models on morphologically rich languages, but that further investigation is needed.
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Authors: Kai Zhang, WangMeng Zuo, Yunjin Chen, Deyu Meng, Lei Zhang
Date: 2016-08-13 00:00:00
Tasks: Color Image Denoising, Denoising, GPU, Image Deblocking, Image Denoising, Image Super-Resolution, JPEG Artifact Correction, Super-Resolution

Discriminative model learning for image denoising has been recently
attracting considerable attentions due to its favorable denoising performance.
In this paper, we take one step forward by investigating the construction of
feed-forward denoising convolutional neural networks (DnCNNs) to embrace the
progress in very deep architecture, learning algorithm, and regularization
method into image denoising. Specifically, residual learning and batch
normalization are utilized to speed up the training process as well as boost
the denoising performance. Different from the existing discriminative denoising
models which usually train a specific model for additive white Gaussian noise
(AWGN) at a certain noise level, our DnCNN model is able to handle Gaussian
denoising with unknown noise level (i.e., blind Gaussian denoising). With the
residual learning strategy, DnCNN implicitly removes the latent clean image in
the hidden layers. This property motivates us to train a single DnCNN model to
tackle with several general image denoising tasks such as Gaussian denoising,
single image super-resolution and JPEG image deblocking. Our extensive
experiments demonstrate that our DnCNN model can not only exhibit high
effectiveness in several general image denoising tasks, but also be efficiently
implemented by benefiting from GPU computing.
Tutorial on Variational Autoencoders
Authors: Carl Doersch
Date: 2016-06-19 00:00:00

In just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions. VAEs are appealing because they are built on top of standard function approximators (neural networks), and can be trained with stochastic gradient descent. VAEs have already shown promise in generating many kinds of complicated data, including handwritten digits, faces, house numbers, CIFAR images, physical models of scenes, segmentation, and predicting the future from static images. This tutorial introduces the intuitions behind VAEs, explains the mathematics behind them, and describes some empirical behavior. No prior knowledge of variational Bayesian methods is assumed.
Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation
Authors: Rudra P. K. Poudel, Pablo Lamata, Giovanni Montana
Date: 2016-08-13 00:00:00
Tasks: Cardiac Segmentation, Segmentation

In cardiac magnetic resonance imaging, fully-automatic segmentation of the
heart enables precise structural and functional measurements to be taken, e.g.
from short-axis MR images of the left-ventricle. In this work we propose a
recurrent fully-convolutional network (RFCN) that learns image representations
from the full stack of 2D slices and has the ability to leverage inter-slice
spatial dependences through internal memory units. RFCN combines anatomical
detection and segmentation into a single architecture that is trained
end-to-end thus significantly reducing computational time, simplifying the
segmentation pipeline, and potentially enabling real-time applications. We
report on an investigation of RFCN using two datasets, including the publicly
available MICCAI 2009 Challenge dataset. Comparisons have been carried out
between fully convolutional networks and deep restricted Boltzmann machines,
including a recurrent version that leverages inter-slice spatial correlation.
Our studies suggest that RFCN produces state-of-the-art results and can
substantially improve the delineation of contours near the apex of the heart.
Fully DNN-based Multi-label regression for audio tagging
Authors: Yong Xu, Qiang Huang, Wenwu Wang, Philip J. B. Jackson, Mark D. Plumbley
Date: 2016-06-24 00:00:00
Tasks: Audio Tagging, Event Detection, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, regression, TAG

Acoustic event detection for content analysis in most cases relies on lots of
labeled data. However, manually annotating data is a time-consuming task, which
thus makes few annotated resources available so far. Unlike audio event
detection, automatic audio tagging, a multi-label acoustic event classification
task, only relies on weakly labeled data. This is highly desirable to some
practical applications using audio analysis. In this paper we propose to use a
fully deep neural network (DNN) framework to handle the multi-label
classification task in a regression way. Considering that only chunk-level
rather than frame-level labels are available, the whole or almost whole frames
of the chunk were fed into the DNN to perform a multi-label regression for the
expected tags. The fully DNN, which is regarded as an encoding function, can
well map the audio features sequence to a multi-tag vector. A deep pyramid
structure was also designed to extract more robust high-level features related
to the target tags. Further improved methods were adopted, such as the Dropout
and background noise aware training, to enhance its generalization capability
for new audio recordings in mismatched environments. Compared with the
conventional Gaussian Mixture Model (GMM) and support vector machine (SVM)
methods, the proposed fully DNN-based method could well utilize the long-term
temporal information with the whole chunk as the input. The results show that
our approach obtained a 15% relative improvement compared with the official
GMM-based method of DCASE 2016 challenge.
Hierarchical learning for DNN-based acoustic scene classification
Authors: Yong Xu, Qiang Huang, Wenwu Wang, Mark D. Plumbley
Date: 2016-07-13 00:00:00
Tasks: Acoustic Scene Classification, Classification, General Classification, Scene Classification

In this paper, we present a deep neural network (DNN)-based acoustic scene
classification framework. Two hierarchical learning methods are proposed to
improve the DNN baseline performance by incorporating the hierarchical taxonomy
information of environmental sounds. Firstly, the parameters of the DNN are
initialized by the proposed hierarchical pre-training. Multi-level objective
function is then adopted to add more constraint on the cross-entropy based loss
function. A series of experiments were conducted on the Task1 of the Detection
and Classification of Acoustic Scenes and Events (DCASE) 2016 challenge. The
final DNN-based system achieved a 22.9% relative improvement on average scene
classification error as compared with the Gaussian Mixture Model (GMM)-based
benchmark system across four standard folds.
Global Deconvolutional Networks for Semantic Segmentation
Authors: Vladimir Nekrasov, Janghoon Ju, Jaesik Choi
Date: 2016-02-12 00:00:00
Tasks: Autonomous Driving, image-classification, Image Classification, Image Segmentation, Object Recognition, Segmentation, Semantic Segmentation

Semantic image segmentation is a principal problem in computer vision, where
the aim is to correctly classify each individual pixel of an image into a
semantic label. Its widespread use in many areas, including medical imaging and
autonomous driving, has fostered extensive research in recent years. Empirical
improvements in tackling this task have primarily been motivated by successful
exploitation of Convolutional Neural Networks (CNNs) pre-trained for image
classification and object recognition. However, the pixel-wise labelling with
CNNs has its own unique challenges: (1) an accurate deconvolution, or
upsampling, of low-resolution output into a higher-resolution segmentation mask
and (2) an inclusion of global information, or context, within locally
extracted features. To address these issues, we propose a novel architecture to
conduct the equivalent of the deconvolution operation globally and acquire
dense predictions. We demonstrate that it leads to improved performance of
state-of-the-art semantic segmentation models on the PASCAL VOC 2012 benchmark,
reaching 74.0% mean IU accuracy on the test set.
Learning Mobile App Usage Routine through Learning Automata
Authors: Ramin Rahnamoun, Reza Rawassizadeh, Arash Maskooki
Date: 2016-08-11 00:00:00
Tasks: Prediction

Since its conception, smart app market has grown exponentially. Success in
the app market depends on many factors among which the quality of the app is a
significant contributor, such as energy use. Nevertheless, smartphones, as a
subset of mobile computing devices. inherit the limited power resource
constraint. Therefore, there is a challenge of maintaining the resource while
increasing the target app quality. This paper introduces Learning Automata (LA)
as an online learning method to learn and predict the app usage routines of the
users. Such prediction can leverage the app cache functionality of the
operating system and thus (i) decreases app launch time and (ii) preserve
battery. Our algorithm, which is an online learning approach, temporally
updates and improves the internal states of itself. In particular, it learns
the transition probabilities between app launching. Each App launching instance
updates the transition probabilities related to that App, and this will result
in improving the prediction. We benefit from a real-world lifelogging dataset
and our experimental results show considerable success with respect to the two
baseline methods that are used currently for smartphone app prediction
approaches.
A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks
Authors: Yetian Chen, Jin Tian, Olga Nikolova, Srinivas Aluru
Date: 2014-08-07 00:00:00
Tasks: Playing the Game of 2048

Exact Bayesian structure discovery in Bayesian networks requires exponential
time and space. Using dynamic programming (DP), the fastest known sequential
algorithm computes the exact posterior probabilities of structural features in
$O(2(d+1)n2^n)$ time and space, if the number of nodes (variables) in the
Bayesian network is $n$ and the in-degree (the number of parents) per node is
bounded by a constant $d$. Here we present a parallel algorithm capable of
computing the exact posterior probabilities for all $n(n-1)$ edges with optimal
parallel space efficiency and nearly optimal parallel time efficiency. That is,
if $p=2^k$ processors are used, the run-time reduces to
$O(5(d+1)n2^{n-k}+k(n-k)^d)$ and the space usage becomes $O(n2^{n-k})$ per
processor. Our algorithm is based the observation that the subproblems in the
sequential DP algorithm constitute a $n$-$D$ hypercube. We take a delicate way
to coordinate the computation of correlated DP procedures such that large
amount of data exchange is suppressed. Further, we develop parallel techniques
for two variants of the well-known \emph{zeta transform}, which have
applications outside the context of Bayesian networks. We demonstrate the
capability of our algorithm on datasets with up to 33 variables and its
scalability on up to 2048 processors. We apply our algorithm to a biological
data set for discovering the yeast pheromone response pathways.