pwc-archive/papers-with-abstracts huggingface dataset:
Noise-Compensated, Bias-Corrected Diffusion Weighted Endorectal Magnetic Resonance Imaging via a Stochastically Fully-Connected Joint Conditional Random Field Model
Authors: Ameneh Boroomand, Mohammad Javad Shafiee, Farzad Khalvati, Masoom A. Haider, Alexander Wong
Date: 2015-12-15 00:00:00
Tasks: Anatomy

Diffusion weighted magnetic resonance imaging (DW-MR) is a powerful tool in
imaging-based prostate cancer screening and detection. Endorectal coils are
commonly used in DW-MR imaging to improve the signal-to-noise ratio (SNR) of
the acquisition, at the expense of significant intensity inhomogeneities (bias
field) that worsens as we move away from the endorectal coil. The presence of
bias field can have a significant negative impact on the accuracy of different
image analysis tasks, as well as prostate tumor localization, thus leading to
increased inter- and intra-observer variability. Retrospective bias correction
approaches are introduced as a more efficient way of bias correction compared
to the prospective methods such that they correct for both of the scanner and
anatomy-related bias fields in MR imaging. Previously proposed retrospective
bias field correction methods suffer from undesired noise amplification that
can reduce the quality of bias-corrected DW-MR image. Here, we propose a
unified data reconstruction approach that enables joint compensation of bias
field as well as data noise in DW-MR imaging. The proposed noise-compensated,
bias-corrected (NCBC) data reconstruction method takes advantage of a novel
stochastically fully connected joint conditional random field (SFC-JCRF) model
to mitigate the effects of data noise and bias field in the reconstructed MR
data. The proposed NCBC reconstruction method was tested on synthetic DW-MR
data, physical DW-phantom as well as real DW-MR data all acquired using
endorectal MR coil. Both qualitative and quantitative analysis illustrated that
the proposed NCBC method can achieve improved image quality when compared to
other tested bias correction methods. As such, the proposed NCBC method may
have potential as a useful retrospective approach for improving the consistency
of image interpretations.
A probabilistic tour of visual attention and gaze shift computational models
Authors: Giuseppe Boccignone
Date: 2016-07-05 00:00:00

In this paper a number of problems are considered which are related to the
modelling of eye guidance under visual attention in a natural setting. From a
crude discussion of a variety of available models spelled in probabilistic
terms, it appears that current approaches in computational vision are hitherto
far from achieving the goal of an active observer relying upon eye guidance to
accomplish real-world tasks. We argue that this challenging goal not only
requires to embody, in a principled way, the problem of eye guidance within the
action/perception loop, but to face the inextricable link tying up visual
attention, emotion and executive control, in so far as recent neurobiological
findings are weighed up.
Machine Learning for Antimicrobial Resistance
Authors: John W. Santerre, James J. Davis, Fangfang Xia, Rick Stevens
Date: 2016-07-05 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Biological datasets amenable to applied machine learning are more available
today than ever before, yet they lack adequate representation in the
Data-for-Good community. Here we present a work in progress case study
performing analysis on antimicrobial resistance (AMR) using standard ensemble
machine learning techniques and note the successes and pitfalls such work
entails. Broadly, applied machine learning (AML) techniques are well suited to
AMR, with classification accuracies ranging from mid-90% to low- 80% depending
on sample size. Additionally, these techniques prove successful at identifying
gene regions known to be associated with the AMR phenotype. We believe that the
extensive amount of biological data available, the plethora of problems
presented, and the global impact of such work merits the consideration of the
Data- for-Good community.
Learning the semantic structure of objects from Web supervision
Authors: David Novotny, Diane Larlus, Andrea Vedaldi
Date: 2016-07-05 00:00:00
Tasks: Navigate

While recent research in image understanding has often focused on recognizing more types of objects, understanding more about the objects is just as important. Recognizing object parts and attributes has been extensively studied before, yet learning large space of such concepts remains elusive due to the high cost of providing detailed object annotations for supervision. The key contribution of this paper is an algorithm to learn the nameable parts of objects automatically, from images obtained by querying Web search engines. The key challenge is the high level of noise in the annotations; to address it, we propose a new unified embedding space where the appearance and geometry of objects and their semantic parts are represented uniformly. Geometric relationships are induced in a soft manner by a rich set of nonsemantic mid-level anchors, bridging the gap between semantic and non-semantic parts. We also show that the resulting embedding provides a visually-intuitive mechanism to navigate the learned concepts and their corresponding images.
Optimal control for a robotic exploration, pick-up and delivery problem
Authors: Vladislav Nenchev, Christos G. Cassandras, Jörg Raisch
Date: 2016-07-05 00:00:00

This paper addresses an optimal control problem for a robot that has to find
and collect a finite number of objects and move them to a depot in minimum
time. The robot has fourth-order dynamics that change instantaneously at any
pick-up or drop-off of an object. The objects are modeled by point masses with
a-priori unknown locations in a bounded two-dimensional space that may contain
unknown obstacles. For this hybrid system, an Optimal Control Problem (OCP) is
approximately solved by a receding horizon scheme, where the derived lower
bound for the cost-to-go is evaluated for the worst and for a probabilistic
case, assuming a uniform distribution of the objects. First, a time-driven
approximate solution based on time and position space discretization and mixed
integer programming is presented. Due to the high computational cost of this
solution, an alternative event-driven approximate approach based on a suitable
motion parameterization and gradient-based optimization is proposed. The
solutions are compared in a numerical example, suggesting that the latter
approach offers a significant computational advantage while yielding similar
qualitative results compared to the former. The methods are particularly
relevant for various robotic applications like automated cleaning, search and
rescue, harvesting or manufacturing.
Scene Text Detection via Holistic, Multi-Channel Prediction
Authors: Cong Yao, Xiang Bai, Nong Sang, Xinyu Zhou, Shuchang Zhou, Zhimin Cao
Date: 2016-06-29 00:00:00
Tasks: Prediction, Scene Text Detection, Semantic Segmentation, Text Detection

Recently, scene text detection has become an active research topic in
computer vision and document analysis, because of its great importance and
significant challenge. However, vast majority of the existing methods detect
text within local regions, typically through extracting character, word or line
level candidates followed by candidate aggregation and false positive
elimination, which potentially exclude the effect of wide-scope and long-range
contextual cues in the scene. To take full advantage of the rich information
available in the whole natural image, we propose to localize text in a holistic
manner, by casting scene text detection as a semantic segmentation problem. The
proposed algorithm directly runs on full images and produces global, pixel-wise
prediction maps, in which detections are subsequently formed. To better make
use of the properties of text, three types of information regarding text
region, individual characters and their relationship are estimated, with a
single Fully Convolutional Network (FCN) model. With such predictions of text
properties, the proposed algorithm can simultaneously handle horizontal,
multi-oriented and curved text in real-world natural images. The experiments on
standard benchmarks, including ICDAR 2013, ICDAR 2015 and MSRA-TD500,
demonstrate that the proposed algorithm substantially outperforms previous
state-of-the-art approaches. Moreover, we report the first baseline result on
the recently-released, large-scale dataset COCO-Text.
Rigid Multiview Varieties
Authors: Michael Joswig, Joe Kileel, Bernd Sturmfels, André Wagner
Date: 2015-09-10 00:00:00

The multiview variety from computer vision is generalized to images by $n$
cameras of points linked by a distance constraint. The resulting
five-dimensional variety lives in a product of $2n$ projective planes. We
determine defining polynomial equations, and we explore generalizations of this
variety to scenarios of interest in applications.
How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?
Authors: Nicolas Goix
Date: 2016-07-05 00:00:00
Tasks: Anomaly Detection, Supervised Anomaly Detection, Unsupervised Anomaly Detection

When sufficient labeled data are available, classical criteria based on
Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be
used to compare the performance of un-supervised anomaly detection algorithms.
However , in many situations, few or no data are labeled. This calls for
alternative criteria one can compute on non-labeled data. In this paper, two
criteria that do not require labels are empirically shown to discriminate
accurately (w.r.t. ROC or PR based criteria) between algorithms. These criteria
are based on existing Excess-Mass (EM) and Mass-Volume (MV) curves, which
generally cannot be well estimated in large dimension. A methodology based on
feature sub-sampling and aggregating is also described and tested, extending
the use of these criteria to high-dimensional datasets and solving major
drawbacks inherent to standard EM and MV curves.
Target-Side Context for Discriminative Models in Statistical Machine Translation
Authors: Aleš Tamchyna, Alexander Fraser, Ondřej Bojar, Marcin Junczys-Dowmunt
Date: 2016-07-05 00:00:00
Proceeding: ACL 2016 8
Tasks: Machine Translation, Translation

Discriminative translation models utilizing source context have been shown to
help statistical machine translation performance. We propose a novel extension
of this work using target context information. Surprisingly, we show that this
model can be efficiently integrated directly in the decoding process. Our
approach scales to large training data sizes and results in consistent
improvements in translation quality on four language pairs. We also provide an
analysis comparing the strengths of the baseline source-context model with our
extended source-context and target-context model and we show that our extension
allows us to better capture morphological coherence. Our work is freely
available as part of Moses.
Minimalist Regression Network with Reinforced Gradients and Weighted Estimates: a Case Study on Parameters Estimation in Automated Welding
Authors: Soheil Keshmiri
Date: 2016-07-05 00:00:00
Tasks: regression

This paper presents a minimalist neural regression network as an aggregate of
independent identical regression blocks that are trained simultaneously.
Moreover, it introduces a new multiplicative parameter, shared by all the
neural units of a given layer, to maintain the quality of its gradients.
Furthermore, it increases its estimation accuracy via learning a weight factor
whose quantity captures the redundancy between the estimated and actual values
at each training iteration. We choose the estimation of the direct weld
parameters of different welding techniques to show a significant improvement in
calculation of these parameters by our model in contrast to state-of-the-arts
techniques in the literature. Furthermore, we demonstrate the ability of our
model to retain its performance when presented with combined data of different
welding techniques. This is a nontrivial result in attaining an scalable model
whose quality of estimation is independent of adopted welding techniques.
Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection
Authors: Meng Fang, Trevor Cohn
Date: 2016-07-05 00:00:00
Proceeding: CONLL 2016 8
Tasks: POS, POS Tagging

Cross lingual projection of linguistic annotation suffers from many sources
of bias and noise, leading to unreliable annotations that cannot be used
directly. In this paper, we introduce a novel approach to sequence tagging that
learns to correct the errors from cross-lingual projection using an explicit
debiasing layer. This is framed as joint learning over two corpora, one tagged
with gold standard and the other with projected tags. We evaluated with only
1,000 tokens tagged with gold standard tags, along with more plentiful parallel
data. Our system equals or exceeds the state-of-the-art on eight simulated
low-resource settings, as well as two real low-resource languages, Malagasy and
Kinyarwanda.
Sequential Dimensionality Reduction for Extracting Localized Features
Authors: Gabriella Casalino, Nicolas Gillis
Date: 2015-05-26 00:00:00
Tasks: Dimensionality Reduction

Linear dimensionality reduction techniques are powerful tools for image
analysis as they allow the identification of important features in a data set.
In particular, nonnegative matrix factorization (NMF) has become very popular
as it is able to extract sparse, localized and easily interpretable features by
imposing an additive combination of nonnegative basis elements. Nonnegative
matrix underapproximation (NMU) is a closely related technique that has the
advantage to identify features sequentially. In this paper, we propose a
variant of NMU that is particularly well suited for image analysis as it
incorporates the spatial information, that is, it takes into account the fact
that neighboring pixels are more likely to be contained in the same features,
and favors the extraction of localized features by looking for sparse basis
elements. We show that our new approach competes favorably with comparable
state-of-the-art techniques on synthetic, facial and hyperspectral image data
sets.
Click Carving: Segmenting Objects in Video with Point Clicks
Authors: Suyog Dutt Jain, Kristen Grauman
Date: 2016-07-05 00:00:00
Tasks: Interactive Video Object Segmentation, Object, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

We present a novel form of interactive video object segmentation where a few
clicks by the user helps the system produce a full spatio-temporal segmentation
of the object of interest. Whereas conventional interactive pipelines take the
user's initialization as a starting point, we show the value in the system
taking the lead even in initialization. In particular, for a given video frame,
the system precomputes a ranked list of thousands of possible segmentation
hypotheses (also referred to as object region proposals) using image and motion
cues. Then, the user looks at the top ranked proposals, and clicks on the
object boundary to carve away erroneous ones. This process iterates (typically
2-3 times), and each time the system revises the top ranked proposal set, until
the user is satisfied with a resulting segmentation mask. Finally, the mask is
propagated across the video to produce a spatio-temporal object tube. On three
challenging datasets, we provide extensive comparisons with both existing work
and simpler alternative methods. In all, the proposed Click Carving approach
strikes an excellent balance of accuracy and human effort. It outperforms all
similarly fast methods, and is competitive or better than those requiring 2 to
12 times the effort.
Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection
Authors: Tim Leathart, Bernhard Pfahringer, Eibe Frank
Date: 2016-04-07 00:00:00
Tasks: General Classification

A system of nested dichotomies is a method of decomposing a multi-class
problem into a collection of binary problems. Such a system recursively splits
the set of classes into two subsets, and trains a binary classifier to
distinguish between each subset. Even though ensembles of nested dichotomies
with random structure have been shown to perform well in practice, using a more
sophisticated class subset selection method can be used to improve
classification accuracy. We investigate an approach to this problem called
random-pair selection, and evaluate its effectiveness compared to other
published methods of subset selection. We show that our method outperforms
other methods in many cases when forming ensembles of nested dichotomies, and
is at least on par in all other cases.
Modelling Context with User Embeddings for Sarcasm Detection in Social Media
Authors: Silvio Amir, Byron C. Wallace, Hao Lyu, Paula Carvalho Mário J. Silva
Date: 2016-07-04 00:00:00
Proceeding: CONLL 2016 8
Tasks: Feature Engineering, Sarcasm Detection

We introduce a deep neural network for automated sarcasm detection. Recent
work has emphasized the need for models to capitalize on contextual features,
beyond lexical and syntactic cues present in utterances. For example, different
speakers will tend to employ sarcasm regarding different subjects and, thus,
sarcasm detection models ought to encode such speaker information. Current
methods have achieved this by way of laborious feature engineering. By
contrast, we propose to automatically learn and then exploit user embeddings,
to be used in concert with lexical signals to recognize sarcasm. Our approach
does not require elaborate feature engineering (and concomitant data scraping);
fitting user embeddings requires only the text from their previous posts. The
experimental results show that our model outperforms a state-of-the-art
approach leveraging an extensive set of carefully crafted features.
Incorporating prior knowledge in medical image segmentation: a survey
Authors: Masoud S. Nosrati, Ghassan Hamarneh
Date: 2016-07-05 00:00:00
Tasks: Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation, Survey

Medical image segmentation, the task of partitioning an image into meaningful
parts, is an important step toward automating medical image analysis and is at
the crux of a variety of medical imaging applications, such as computer aided
diagnosis, therapy planning and delivery, and computer aided interventions.
However, the existence of noise, low contrast and objects' complexity in
medical images are critical obstacles that stand in the way of achieving an
ideal segmentation system. Incorporating prior knowledge into image
segmentation algorithms has proven useful for obtaining more accurate and
plausible results. This paper surveys the different types of prior knowledge
that have been utilized in different segmentation frameworks. We focus our
survey on optimization-based methods that incorporate prior information into
their frameworks. We review and compare these methods in terms of the types of
prior employed, the domain of formulation (continuous vs. discrete), and the
optimization techniques (global vs. local). We also created an interactive
online database of existing works and categorized them based on the type of
prior knowledge they use. Our website is interactive so that researchers can
contribute to keep the database up to date. We conclude the survey by
discussing different aspects of designing an energy functional for image
segmentation, open problems, and future perspectives.
Compliant Conditions for Polynomial Time Approximation of Operator Counts
Authors: Tathagata Chakraborti, Sarath Sreedharan, Sailik Sengupta, T. K. Satish Kumar, Subbarao Kambhampati
Date: 2016-05-25 00:00:00
Tasks: compressed sensing

In this paper, we develop a computationally simpler version of the operator
count heuristic for a particular class of domains. The contribution of this
abstract is threefold, we (1) propose an efficient closed form approximation to
the operator count heuristic using the Lagrangian dual; (2) leverage compressed
sensing techniques to obtain an integer approximation for operator counts in
polynomial time; and (3) discuss the relationship of the proposed formulation
to existing heuristics and investigate properties of domains where such
approaches appear to be useful.
Similarity-based Text Recognition by Deeply Supervised Siamese Network
Authors: Ehsan Hosseini-Asl, Angshuman Guha
Date: 2015-11-13 00:00:00
Tasks: Spelling Correction

In this paper, we propose a new text recognition model based on measuring the
visual similarity of text and predicting the content of unlabeled texts. First
a Siamese convolutional network is trained with deep supervision on a labeled
training dataset. This network projects texts into a similarity manifold. The
Deeply Supervised Siamese network learns visual similarity of texts. Then a
K-nearest neighbor classifier is used to predict unlabeled text based on
similarity distance to labeled texts. The performance of the model is evaluated
on three datasets of machine-print and hand-written text combined. We
demonstrate that the model reduces the cost of human estimation by $50\%-85\%$.
The error of the system is less than $0.5\%$. The proposed model outperform
conventional Siamese network by finding visually-similar barely-readable and
readable text, e.g. machine-printed, handwritten, due to deep supervision. The
results also demonstrate that the predicted labels are sometimes better than
human labels e.g. spelling correction.
Streaming Algorithms for News and Scientific Literature Recommendation: Submodular Maximization with a d-Knapsack Constraint
Authors: Qilian Yu, Easton Li Xu, Shuguang Cui
Date: 2016-03-17 00:00:00
Tasks: Combinatorial Optimization, News Recommendation

Submodular maximization problems belong to the family of combinatorial
optimization problems and enjoy wide applications. In this paper, we focus on
the problem of maximizing a monotone submodular function subject to a
$d$-knapsack constraint, for which we propose a streaming algorithm that
achieves a $\left(\frac{1}{1+2d}-\epsilon\right)$-approximation of the optimal
value, while it only needs one single pass through the dataset without storing
all the data in the memory. In our experiments, we extensively evaluate the
effectiveness of our proposed algorithm via two applications: news
recommendation and scientific literature recommendation. It is observed that
the proposed streaming algorithm achieves both execution speedup and memory
saving by several orders of magnitude, compared with existing approaches.
EmoFit: Affect Monitoring System for Sedentary Jobs
Authors: Amol Patwardhan, Gerald Knapp
Date: 2016-07-05 00:00:00

Emotional and physical well-being at workplace is important for a positive
work environment and higher productivity. Jobs such as software programming
lead to a sedentary lifestyle and require high interaction with computers.
Working at the same job for years can cause a feeling of intellectual
stagnation and lack of drive. Many employees experience lack of motivation,
mild to extreme depression due to reasons such as aversion towards job
responsibilities and incompatibility with coworkers or boss. This research
proposed an affect monitoring system EmoFit that would play the role of
psychological and physical health trainer. The day to day computer activity and
body language was analyzed to detect the physical and emotional well-being of
the user. Keystrokes, activity interruptions, eye tracking, facial expressions,
body posture and speech were monitored to gauge the users health. The system
also provided activities such as at-desk exercise and stress relief game and
motivational quotes in an attempt to promote users well-being. The experimental
results and positive feedback from test subjects showed that EmoFit would help
improve emotional and physical well-being at jobs that involve significant
computer usage.