pwc-archive/papers-with-abstracts huggingface dataset:
A knowledge representation meta-model for rule-based modelling of signalling networks
Authors: Adrien Basso-Blandin, Walter Fontana, Russ Harmer
Date: 2016-03-03 00:00:00

The study of cellular signalling pathways and their deregulation in disease
states, such as cancer, is a large and extremely complex task. Indeed, these
systems involve many parts and processes but are studied piecewise and their
literatures and data are consequently fragmented, distributed and sometimes--at
least apparently--inconsistent. This makes it extremely difficult to build
significant explanatory models with the result that effects in these systems
that are brought about by many interacting factors are poorly understood.
  The rule-based approach to modelling has shown some promise for the
representation of the highly combinatorial systems typically found in
signalling where many of the proteins are composed of multiple binding domains,
capable of simultaneous interactions, and/or peptide motifs controlled by
post-translational modifications. However, the rule-based approach requires
highly detailed information about the precise conditions for each and every
interaction which is rarely available from any one single source. Rather, these
conditions must be painstakingly inferred and curated, by hand, from
information contained in many papers--each of which contains only part of the
story.
  In this paper, we introduce a graph-based meta-model, attuned to the
representation of cellular signalling networks, which aims to ease this massive
cognitive burden on the rule-based curation process. This meta-model is a
generalization of that used by Kappa and BNGL which allows for the flexible
representation of knowledge at various levels of granularity. In particular, it
allows us to deal with information which has either too little, or too much,
detail with respect to the strict rule-based meta-model. Our approach provides
a basis for the gradual aggregation of fragmented biological knowledge
extracted from the literature into an instance of the meta-model from which we
can define an automated translation into executable Kappa programs.
On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach
Authors: Sam Davanloo Tajbakhsh, Necdet Serhat Aybat, Enrique del Castillo
Date: 2014-05-21 00:00:00
Tasks: parameter estimation

Iterative methods for fitting a Gaussian Random Field (GRF) model via maximum likelihood (ML) estimation requires solving a nonconvex optimization problem. The problem is aggravated for anisotropic GRFs where the number of covariance function parameters increases with the dimension. Even evaluation of the likelihood function requires $O(n^3)$ floating point operations, where $n$ denotes the number of data locations. In this paper, we propose a new two-stage procedure to estimate the parameters of second-order stationary GRFs. First, a convex likelihood problem regularized with a weighted $\ell_1$-norm, utilizing the available distance information between observation locations, is solved to fit a sparse precision (inverse covariance) matrix to the observed data. Second, the parameters of the covariance function are estimated by solving a least squares problem. Theoretical error bounds for the solutions of stage I and II problems are provided, and their tightness are investigated.
Interactive and Scale Invariant Segmentation of the Rectum/Sigmoid via User-Defined Templates
Authors: Tobias Lüddemann, Jan Egger
Date: 2016-03-03 00:00:00
Tasks: Segmentation

Among all types of cancer, gynecological malignancies belong to the 4th most
frequent type of cancer among women. Besides chemotherapy and external beam
radiation, brachytherapy is the standard procedure for the treatment of these
malignancies. In the progress of treatment planning, localization of the tumor
as the target volume and adjacent organs of risks by segmentation is crucial to
accomplish an optimal radiation distribution to the tumor while simultaneously
preserving healthy tissue. Segmentation is performed manually and represents a
time-consuming task in clinical daily routine. This study focuses on the
segmentation of the rectum/sigmoid colon as an Organ-At-Risk in gynecological
brachytherapy. The proposed segmentation method uses an interactive,
graph-based segmentation scheme with a user-defined template. The scheme
creates a directed two dimensional graph, followed by the minimal cost closed
set computation on the graph, resulting in an outlining of the rectum. The
graphs outline is dynamically adapted to the last calculated cut. Evaluation
was performed by comparing manual segmentations of the rectum/sigmoid colon to
results achieved with the proposed method. The comparison of the algorithmic to
manual results yielded to a Dice Similarity Coefficient value of 83.85+/-4.08%,
in comparison to 83.97+/-8.08% for the comparison of two manual segmentations
of the same physician. Utilizing the proposed methodology resulted in a median
time of 128 seconds per dataset, compared to 300 seconds needed for pure manual
segmentation.
Cellular Automata Segmentation of the Boundary between the Compacta of Vertebral Bodies and Surrounding Structures
Authors: Jan Egger, Christopher Nimsky
Date: 2016-03-03 00:00:00
Tasks: Segmentation

Due to the aging population, spinal diseases get more and more common
nowadays; e.g., lifetime risk of osteoporotic fracture is 40% for white women
and 13% for white men in the United States. Thus the numbers of surgical spinal
procedures are also increasing with the aging population and precise diagnosis
plays a vital role in reducing complication and recurrence of symptoms. Spinal
imaging of vertebral column is a tedious process subjected to interpretation
errors. In this contribution, we aim to reduce time and error for vertebral
interpretation by applying and studying the GrowCut-algorithm for boundary
segmentation between vertebral body compacta and surrounding structures.
GrowCut is a competitive region growing algorithm using cellular automata. For
our study, vertebral T2-weighted Magnetic Resonance Imaging (MRI) scans were
first manually outlined by neurosurgeons. Then, the vertebral bodies were
segmented in the medical images by a GrowCut-trained physician using the
semi-automated GrowCut-algorithm. Afterwards, results of both segmentation
processes were compared using the Dice Similarity Coefficient (DSC) and the
Hausdorff Distance (HD) which yielded to a DSC of 82.99+/-5.03% and a HD of
18.91+/-7.2 voxel, respectively. In addition, the times have been measured
during the manual and the GrowCut segmentations, showing that a
GrowCut-segmentation - with an average time of less than six minutes
(5.77+/-0.73) - is significantly shorter than a pure manual outlining.
Estimating the number of unseen species: A bird in the hand is worth $\log n $ in the bush
Authors: Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu
Date: 2015-11-23 00:00:00

Estimating the number of unseen species is an important problem in many
scientific endeavors. Its most popular formulation, introduced by Fisher, uses
$n$ samples to predict the number $U$ of hitherto unseen species that would be
observed if $t\cdot n$ new samples were collected. Of considerable interest is
the largest ratio $t$ between the number of new and existing samples for which
$U$ can be accurately predicted.
  In seminal works, Good and Toulmin constructed an intriguing estimator that
predicts $U$ for all $t\le 1$, thereby showing that the number of species can
be estimated for a population twice as large as that observed. Subsequently
Efron and Thisted obtained a modified estimator that empirically predicts $U$
even for some $t>1$, but without provable guarantees.
  We derive a class of estimators that $\textit{provably}$ predict $U$ not just
for constant $t>1$, but all the way up to $t$ proportional to $\log n$. This
shows that the number of species can be estimated for a population $\log n$
times larger than that observed, a factor that grows arbitrarily large as $n$
increases. We also show that this range is the best possible and that the
estimators' mean-square error is optimal up to constants for any $t$. Our
approach yields the first provable guarantee for the Efron-Thisted estimator
and, in addition, a variant which achieves stronger theoretical and
experimental performance than existing methodologies on a variety of synthetic
and real datasets.
  The estimators we derive are simple linear estimators that are computable in
time proportional to $n$. The performance guarantees hold uniformly for all
distributions, and apply to all four standard sampling models commonly used
across various scientific disciplines: multinomial, Poisson, hypergeometric,
and Bernoulli product.
PCANet: An energy perspective
Authors: Jiasong Wu, Shijie Qiu, Youyong Kong, Longyu Jiang, Lotfi Senhadji, Huazhong Shu
Date: 2016-03-03 00:00:00
Tasks: General Classification

The principal component analysis network (PCANet), which is one of the
recently proposed deep learning architectures, achieves the state-of-the-art
classification accuracy in various databases. However, the explanation of the
PCANet is lacked. In this paper, we try to explain why PCANet works well from
energy perspective point of view based on a set of experiments. The impact of
various parameters on the error rate of PCANet is analyzed in depth. It was
found that this error rate is correlated with the logarithm of energy of image.
The proposed energy explanation approach can be used as a testing method for
checking if every step of the constructed networks is necessary.
LiDAR Ground Filtering Algorithm for Urban Areas Using Scan Line Based Segmentation
Authors: Lei Wang, Yongun Zhang
Date: 2016-03-02 00:00:00
Tasks: Denoising

This paper addresses the task of separating ground points from airborne LiDAR
point cloud data in urban areas. A novel ground filtering method using scan
line segmentation is proposed here, which we call SLSGF. It utilizes the scan
line information in LiDAR data to segment the LiDAR data. The similarity
measurements are designed to make it possible to segment complex roof
structures into a single segment as much as possible so the topological
relationships between the roof and the ground are simpler, which will benefit
the labeling process. In the labeling process, the initial ground segments are
detected and a coarse to fine labeling scheme is applied. Data from ISPRS 2011
are used to test the accuracy of SLSGF; and our analytical and experimental
results show that this method is computationally-efficient and
noise-insensitive, thereby making a denoising process unnecessary before
filtering.
Counter-fitting Word Vectors to Linguistic Constraints
Authors: Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Lina Rojas-Barahona, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, Steve Young
Date: 2016-03-02 00:00:00
Proceeding: NAACL 2016 6
Tasks: Dialogue State Tracking, Semantic Similarity, Semantic Textual Similarity

In this work, we present a novel counter-fitting method which injects
antonymy and synonymy constraints into vector space representations in order to
improve the vectors' capability for judging semantic similarity. Applying this
method to publicly available pre-trained word vectors leads to a new state of
the art performance on the SimLex-999 dataset. We also show how the method can
be used to tailor the word vector space for the downstream task of dialogue
state tracking, resulting in robust improvements across different dialogue
domains.
Shallow and Deep Convolutional Networks for Saliency Prediction
Authors: Junting Pan, Kevin McGuinness, Elisa Sayrol, Noel O'Connor, Xavier Giro-i-Nieto
Date: 2016-03-02 00:00:00
Proceeding: CVPR 2016 6
Tasks: Prediction, Saliency Prediction

The prediction of salient areas in images has been traditionally addressed
with hand-crafted features based on neuroscience principles. This paper,
however, addresses the problem with a completely data-driven approach by
training a convolutional neural network (convnet). The learning process is
formulated as a minimization of a loss function that measures the Euclidean
distance of the predicted saliency map with the provided ground truth. The
recent publication of large datasets of saliency prediction has provided enough
data to train end-to-end architectures that are both fast and accurate. Two
designs are proposed: a shallow convnet trained from scratch, and a another
deeper solution whose first three layers are adapted from another network
trained for classification. To the authors knowledge, these are the first
end-to-end CNNs trained and tested for the purpose of saliency prediction.
Automatic segmentation of lizard spots using an active contour model
Authors: Jhony Giraldo, Augusto Salazar
Date: 2016-03-02 00:00:00
Tasks: Segmentation

Animal biometrics is a challenging task. In the literature, many algorithms
have been used, e.g. penguin chest recognition, elephant ears recognition and
leopard stripes pattern recognition, but to use technology to a large extent in
this area of research, still a lot of work has to be done. One important target
in animal biometrics is to automate the segmentation process, so in this paper
we propose a segmentation algorithm for extracting the spots of Diploglossus
millepunctatus, an endangered lizard species. The automatic segmentation is
achieved with a combination of preprocessing, active contours and morphology.
The parameters of each stage of the segmentation algorithm are found using an
optimization procedure, which is guided by the ground truth. The results show
that automatic segmentation of spots is possible. A 78.37 % of correct
segmentation in average is reached.
Unconstrained Face Verification using Deep CNN Features
Authors: Jun-Cheng Chen, Vishal M. Patel, Rama Chellappa
Date: 2015-08-07 00:00:00
Tasks: Face Verification

In this paper, we present an algorithm for unconstrained face verification
based on deep convolutional features and evaluate it on the newly released
IARPA Janus Benchmark A (IJB-A) dataset. The IJB-A dataset includes real-world
unconstrained faces from 500 subjects with full pose and illumination
variations which are much harder than the traditional Labeled Face in the Wild
(LFW) and Youtube Face (YTF) datasets. The deep convolutional neural network
(DCNN) is trained using the CASIA-WebFace dataset. Extensive experiments on the
IJB-A dataset are provided.
Multimodal sparse representation learning and applications
Authors: Miriam Cha, Youngjune Gwon, H. T. Kung
Date: 2015-11-19 00:00:00
Tasks: Classification, Denoising, Dictionary Learning, Event Detection, General Classification, Image Denoising, Representation Learning, Retrieval, Sentiment Analysis, Sentiment Classification

Unsupervised methods have proven effective for discriminative tasks in a
single-modality scenario. In this paper, we present a multimodal framework for
learning sparse representations that can capture semantic correlation between
modalities. The framework can model relationships at a higher level by forcing
the shared sparse representation. In particular, we propose the use of joint
dictionary learning technique for sparse coding and formulate the joint
representation for concision, cross-modal representations (in case of a missing
modality), and union of the cross-modal representations. Given the accelerated
growth of multimodal data posted on the Web such as YouTube, Wikipedia, and
Twitter, learning good multimodal features is becoming increasingly important.
We show that the shared representations enabled by our framework substantially
improve the classification performance under both unimodal and multimodal
settings. We further show how deep architectures built on the proposed
framework are effective for the case of highly nonlinear correlations between
modalities. The effectiveness of our approach is demonstrated experimentally in
image denoising, multimedia event detection and retrieval on the TRECVID
dataset (audio-video), category classification on the Wikipedia dataset
(image-text), and sentiment classification on PhotoTweet (image-text).
Belief and Truth in Hypothesised Behaviours
Authors: Stefano V. Albrecht, Jacob W. Crandall, Subramanian Ramamoorthy
Date: 2015-07-28 00:00:00

There is a long history in game theory on the topic of Bayesian or "rational"
learning, in which each player maintains beliefs over a set of alternative
behaviours, or types, for the other players. This idea has gained increasing
interest in the artificial intelligence (AI) community, where it is used as a
method to control a single agent in a system composed of multiple agents with
unknown behaviours. The idea is to hypothesise a set of types, each specifying
a possible behaviour for the other agents, and to plan our own actions with
respect to those types which we believe are most likely, given the observed
actions of the agents. The game theory literature studies this idea primarily
in the context of equilibrium attainment. In contrast, many AI applications
have a focus on task completion and payoff maximisation. With this perspective
in mind, we identify and address a spectrum of questions pertaining to belief
and truth in hypothesised types. We formulate three basic ways to incorporate
evidence into posterior beliefs and show when the resulting beliefs are
correct, and when they may fail to be correct. Moreover, we demonstrate that
prior beliefs can have a significant impact on our ability to maximise payoffs
in the long-term, and that they can be computed automatically with consistent
performance effects. Furthermore, we analyse the conditions under which we are
able complete our task optimally, despite inaccuracies in the hypothesised
types. Finally, we show how the correctness of hypothesised types can be
ascertained during the interaction via an automated statistical analysis.
Flies as Ship Captains? Digital Evolution Unravels Selective Pressures to Avoid Collision in Drosophila
Authors: Ali Tehrani-Saleh, Christoph Adami
Date: 2016-03-02 00:00:00
Tasks: Collision Avoidance

Flies that walk in a covered planar arena on straight paths avoid colliding
with each other, but which of the two flies stops is not random.
High-throughput video observations, coupled with dedicated experiments with
controlled robot flies have revealed that flies utilize the type of optic flow
on their retina as a determinant of who should stop, a strategy also used by
ship captains to determine which of two ships on a collision course should
throw engines in reverse. We use digital evolution to test whether this
strategy evolves when collision avoidance is the sole penalty. We find that the
strategy does indeed evolve in a narrow range of cost/benefit ratios, for
experiments in which the "regressive motion" cue is error free. We speculate
that these stringent conditions may not be sufficient to evolve the strategy in
real flies, pointing perhaps to auxiliary costs and benefits not modeled in our
study
Finding Preference Profiles of Condorcet Dimension $k$ via SAT
Authors: Christian Geist
Date: 2014-02-18 00:00:00

Condorcet winning sets are a set-valued generalization of the well-known
concept of a Condorcet winner. As supersets of Condorcet winning sets are
always Condorcet winning sets themselves, an interesting property of preference
profiles is the size of the smallest Condorcet winning set they admit. This
smallest size is called the Condorcet dimension of a preference profile. Since
little is known about profiles that have a certain Condorcet dimension, we show
in this paper how the problem of finding a preference profile that has a given
Condorcet dimension can be encoded as a satisfiability problem and solved by a
SAT solver. Initial results include a minimal example of a preference profile
of Condorcet dimension 3, improving previously known examples both in terms of
the number of agents as well as alternatives. Due to the high complexity of
such problems it remains open whether a preference profile of Condorcet
dimension 4 exists.
Automatic Differentiation Variational Inference
Authors: Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
Date: 2016-03-02 00:00:00
Tasks: Probabilistic Programming, Variational Inference

Probabilistic modeling is iterative. A scientist posits a simple model, fits
it to her data, refines it according to her analysis, and repeats. However,
fitting complex models to large data is a bottleneck in this process. Deriving
algorithms for new models can be both mathematically and computationally
challenging, which makes it difficult to efficiently cycle through the steps.
To this end, we develop automatic differentiation variational inference (ADVI).
Using our method, the scientist only provides a probabilistic model and a
dataset, nothing else. ADVI automatically derives an efficient variational
inference algorithm, freeing the scientist to refine and explore many models.
ADVI supports a broad class of models-no conjugacy assumptions are required. We
study ADVI across ten different models and apply it to a dataset with millions
of observations. ADVI is integrated into Stan, a probabilistic programming
system; it is available for immediate use.
Equity forecast: Predicting long term stock price movement using machine learning
Authors: Nikola Milosevic
Date: 2016-03-02 00:00:00
Tasks: BIG-bench Machine Learning

Long term investment is one of the major investment strategies. However,
calculating intrinsic value of some company and evaluating shares for long term
investment is not easy, since analyst have to care about a large number of
financial indicators and evaluate them in a right manner. So far, little help
in predicting the direction of the company value over the longer period of time
has been provided from the machines. In this paper we present a machine
learning aided approach to evaluate the equity's future price over the long
time. Our method is able to correctly predict whether some company's value will
be 10% higher or not over the period of one year in 76.5% of cases.
Continuous Deep Q-Learning with Model-based Acceleration
Authors: Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine
Date: 2016-03-02 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-free reinforcement learning has been successfully applied to a range of
challenging problems, and has recently been extended to handle large neural
network policies and value functions. However, the sample complexity of
model-free algorithms, particularly when using high-dimensional function
approximators, tends to limit their applicability to physical systems. In this
paper, we explore algorithms and representations to reduce the sample
complexity of deep reinforcement learning for continuous control tasks. We
propose two complementary techniques for improving the efficiency of such
algorithms. First, we derive a continuous variant of the Q-learning algorithm,
which we call normalized adantage functions (NAF), as an alternative to the
more commonly used policy gradient and actor-critic methods. NAF representation
allows us to apply Q-learning with experience replay to continuous tasks, and
substantially improves performance on a set of simulated robotic control tasks.
To further improve the efficiency of our approach, we explore the use of
learned models for accelerating model-free reinforcement learning. We show that
iteratively refitted local linear models are especially effective for this, and
demonstrate substantially faster learning on domains where such models are
applicable.