pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning for Survival Analysis: A Survey
Authors: Ping Wang, Yan Li, Chandan K. Reddy
Date: 2017-08-15 00:00:00
Tasks: BIG-bench Machine Learning, Survey, Survival Analysis

Accurately predicting the time of occurrence of an event of interest is a
critical problem in longitudinal data analysis. One of the main challenges in
this context is the presence of instances whose event outcomes become
unobservable after a certain time point or when some instances do not
experience any event during the monitoring period. Such a phenomenon is called
censoring which can be effectively handled using survival analysis techniques.
Traditionally, statistical approaches have been widely developed in the
literature to overcome this censoring issue. In addition, many machine learning
algorithms are adapted to effectively handle survival data and tackle other
challenging problems that arise in real-world data. In this survey, we provide
a comprehensive and structured review of the representative statistical methods
along with the machine learning techniques used in survival analysis and
provide a detailed taxonomy of the existing methods. We also discuss several
topics that are closely related to survival analysis and illustrate several
successful applications in various real-world application domains. We hope that
this paper will provide a more thorough understanding of the recent advances in
survival analysis and offer some guidelines on applying these approaches to
solve new problems that arise in applications with censored data.
Deep Learning the Ising Model Near Criticality
Authors: Alan Morningstar, Roger G. Melko
Date: 2017-08-15 00:00:00
Tasks: Deep Learning, model

It is well established that neural networks with deep architectures perform
better than shallow networks for many tasks in machine learning. In statistical
physics, while there has been recent interest in representing physical data
with generative modelling, the focus has been on shallow neural networks. A
natural question to ask is whether deep neural networks hold any advantage over
shallow networks in representing such data. We investigate this question by
using unsupervised, generative graphical models to learn the probability
distribution of a two-dimensional Ising system. Deep Boltzmann machines, deep
belief networks, and deep restricted Boltzmann networks are trained on thermal
spin configurations from this system, and compared to the shallow architecture
of the restricted Boltzmann machine. We benchmark the models, focussing on the
accuracy of generating energetic observables near the phase transition, where
these quantities are most difficult to approximate. Interestingly, after
training the generative networks, we observe that the accuracy essentially
depends only on the number of neurons in the first hidden layer of the network,
and not on other model details such as network depth or model type. This is
evidence that shallow networks are more efficient than deep networks at
representing physical probability distributions associated with Ising systems
near criticality.
Gland Segmentation in Histopathology Images Using Random Forest Guided Boundary Construction
Authors: Rohith AP, Salman S. Khan, Kumar Anubhav, Angshuman Paul
Date: 2017-05-14 00:00:00
Tasks: Segmentation

Grading of cancer is important to know the extent of its spread. Prior to
grading, segmentation of glandular structures is important. Manual segmentation
is a time consuming process and is subject to observer bias. Hence, an
automated process is required to segment the gland structures. These glands
show a large variation in shape size and texture. This makes the task
challenging as the glands cannot be segmented using mere morphological
operations and conventional segmentation mechanisms. In this project we propose
a method which detects the boundary epithelial cells of glands and then a novel
approach is used to construct the complete gland boundary. The region enclosed
within the boundary can then be obtained to get the segmented gland regions.
Segmentation-Aware Convolutional Networks Using Local Attention Masks
Authors: Adam W. Harley, Konstantinos G. Derpanis, Iasonas Kokkinos
Date: 2017-08-15 00:00:00
Proceeding: ICCV 2017 10
Tasks: Optical Flow Estimation, Segmentation, Semantic Segmentation

We introduce an approach to integrate segmentation information within a
convolutional neural network (CNN). This counter-acts the tendency of CNNs to
smooth information across regions and increases their spatial precision. To
obtain segmentation information, we set up a CNN to provide an embedding space
where region co-membership can be estimated based on Euclidean distance. We use
these embeddings to compute a local attention mask relative to every neuron
position. We incorporate such masks in CNNs and replace the convolution
operation with a "segmentation-aware" variant that allows a neuron to
selectively attend to inputs coming from its own region. We call the resulting
network a segmentation-aware CNN because it adapts its filters at each image
point according to local segmentation cues. We demonstrate the merit of our
method on two widely different dense prediction tasks, that involve
classification (semantic segmentation) and regression (optical flow). Our
results show that in semantic segmentation we can match the performance of
DenseCRFs while being faster and simpler, and in optical flow we obtain clearly
sharper responses than networks that do not use local attention masks. In both
cases, segmentation-aware convolution yields systematic improvements over
strong baselines. Source code for this work is available online at
http://cs.cmu.edu/~aharley/segaware.
Gold Standard Online Debates Summaries and First Experiments Towards Automatic Summarization of Online Debate Data
Authors: Nattapong Sanchan, Ahmet Aker, Kalina Bontcheva
Date: 2017-08-15 00:00:00
Tasks: Articles, Extractive Summarization, Information Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity, Text Summarization

Usage of online textual media is steadily increasing. Daily, more and more
news stories, blog posts and scientific articles are added to the online
volumes. These are all freely accessible and have been employed extensively in
multiple research areas, e.g. automatic text summarization, information
retrieval, information extraction, etc. Meanwhile, online debate forums have
recently become popular, but have remained largely unexplored. For this reason,
there are no sufficient resources of annotated debate data available for
conducting research in this genre. In this paper, we collected and annotated
debate data for an automatic summarization task. Similar to extractive gold
standard summary generation our data contains sentences worthy to include into
a summary. Five human annotators performed this task. Inter-annotator
agreement, based on semantic similarity, is 36% for Cohen's kappa and 48% for
Krippendorff's alpha. Moreover, we also implement an extractive summarization
system for online debates and discuss prominent features for the task of
summarizing online debate data automatically.
Automatic Summarization of Online Debates
Authors: Nattapong Sanchan, Ahmet Aker, Kalina Bontcheva
Date: 2017-08-15 00:00:00
Proceeding: RANLP 2017 9
Tasks: Clustering, Text Summarization

Debate summarization is one of the novel and challenging research areas in
automatic text summarization which has been largely unexplored. In this paper,
we develop a debate summarization pipeline to summarize key topics which are
discussed or argued in the two opposing sides of online debates. We view that
the generation of debate summaries can be achieved by clustering, cluster
labeling, and visualization. In our work, we investigate two different
clustering approaches for the generation of the summaries. In the first
approach, we generate the summaries by applying purely term-based clustering
and cluster labeling. The second approach makes use of X-means for clustering
and Mutual Information for labeling the clusters. Both approaches are driven by
ontologies. We visualize the results using bar charts. We think that our
results are a smooth entry for users aiming to receive the first impression
about what is discussed within a debate topic containing waste number of
argumentations.
Diverse Weighted Bipartite b-Matching
Authors: Faez Ahmed, John P. Dickerson, Mark Fuge
Date: 2017-02-23 00:00:00
Tasks: Diversity, Fairness

Bipartite matching, where agents on one side of a market are matched to
agents or items on the other, is a classical problem in computer science and
economics, with widespread application in healthcare, education, advertising,
and general resource allocation. A practitioner's goal is typically to maximize
a matching market's economic efficiency, possibly subject to some fairness
requirements that promote equal access to resources. A natural balancing act
exists between fairness and efficiency in matching markets, and has been the
subject of much research.
  In this paper, we study a complementary goal---balancing diversity and
efficiency---in a generalization of bipartite matching where agents on one side
of the market can be matched to sets of agents on the other. Adapting a
classical definition of the diversity of a set, we propose a quadratic
programming-based approach to solving a supermodular minimization problem that
balances diversity and total weight of the solution. We also provide a scalable
greedy algorithm with theoretical performance bounds. We then define the price
of diversity, a measure of the efficiency loss due to enforcing diversity, and
give a worst-case theoretical bound. Finally, we demonstrate the efficacy of
our methods on three real-world datasets, and show that the price of diversity
is not bad in practice.
End-to-End Neural Segmental Models for Speech Recognition
Authors: Hao Tang, Liang Lu, Lingpeng Kong, Kevin Gimpel, Karen Livescu, Chris Dyer, Noah A. Smith, Steve Renals
Date: 2017-08-01 00:00:00
Tasks: Decoder, speech-recognition, Speech Recognition

Segmental models are an alternative to frame-based models for sequence
prediction, where hypothesized path weights are based on entire segment scores
rather than a single frame at a time. Neural segmental models are segmental
models that use neural network-based weight functions. Neural segmental models
have achieved competitive results for speech recognition, and their end-to-end
training has been explored in several studies. In this work, we review neural
segmental models, which can be viewed as consisting of a neural network-based
acoustic encoder and a finite-state transducer decoder. We study end-to-end
segmental models with different weight functions, including ones based on
frame-level neural classifiers and on segmental recurrent neural networks. We
study how reducing the search space size impacts performance under different
weight functions. We also compare several loss functions for end-to-end
training. Finally, we explore training approaches, including multi-stage vs.
end-to-end training and multitask training that combines segmental and
frame-level losses.
BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations
Authors: Georgios Smaragdos, Georgios Chatzikonstantis, Rahul Kukreja, Harry Sidiropoulos, Dimitrios Rodopoulos, Ioannis Sourdis, Zaid Al-Ars, Christoforos Kachris, Dimitrios Soudris, Chris I. De Zeeuw, Christos Strydis
Date: 2016-12-05 00:00:00
Tasks: Diversity, GPU

Objective: The advent of High-Performance Computing (HPC) in recent years has
led to its increasing use in brain study through computational models. The
scale and complexity of such models are constantly increasing, leading to
challenging computational requirements. Even though modern HPC platforms can
often deal with such challenges, the vast diversity of the modeling field does
not permit for a single acceleration (or homogeneous) platform to effectively
address the complete array of modeling requirements. Approach: In this paper we
propose and build BrainFrame, a heterogeneous acceleration platform,
incorporating three distinct acceleration technologies, a Dataflow Engine, a
Xeon Phi and a GP-GPU. The PyNN framework is also integrated into the platform.
As a challenging proof of concept, we analyze the performance of BrainFrame on
different instances of a state-of-the-art neuron model, modeling the Inferior-
Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley
representation. The model instances take into account not only the neuronal-
network dimensions but also different network-connectivity circumstances that
can drastically change application workload characteristics. Main results: The
synthetic approach of three HPC technologies demonstrated that BrainFrame is
better able to cope with the modeling diversity encountered. Our performance
analysis shows clearly that the model directly affect performance and all three
technologies are required to cope with all the model use cases.
Database of Parliamentary Speeches in Ireland, 1919-2013
Authors: Alexander Herzog, Slava J. Mikhaylov
Date: 2017-08-15 00:00:00

We present a database of parliamentary debates that contains the complete
record of parliamentary speeches from D\'ail \'Eireann, the lower house and
principal chamber of the Irish parliament, from 1919 to 2013. In addition, the
database contains background information on all TDs (Teachta D\'ala, members of
parliament), such as their party affiliations, constituencies and office
positions. The current version of the database includes close to 4.5 million
speeches from 1,178 TDs. The speeches were downloaded from the official
parliament website and further processed and parsed with a Python script.
Background information on TDs was collected from the member database of the
parliament website. Data on cabinet positions (ministers and junior ministers)
was collected from the official website of the government. A record linkage
algorithm and human coders were used to match TDs and ministers.
Resilient Linear Classification: An Approach to Deal with Attacks on Training Data
Authors: Sangdon Park, James Weimer, Insup Lee
Date: 2017-08-10 00:00:00
Tasks: Autonomous Vehicles, Classification, energy management, General Classification, Management

Data-driven techniques are used in cyber-physical systems (CPS) for
controlling autonomous vehicles, handling demand responses for energy
management, and modeling human physiology for medical devices. These
data-driven techniques extract models from training data, where their
performance is often analyzed with respect to random errors in the training
data. However, if the training data is maliciously altered by attackers, the
effect of these attacks on the learning algorithms underpinning data-driven CPS
have yet to be considered. In this paper, we analyze the resilience of
classification algorithms to training data attacks. Specifically, a generic
metric is proposed that is tailored to measure resilience of classification
algorithms with respect to worst-case tampering of the training data. Using the
metric, we show that traditional linear classification algorithms are resilient
under restricted conditions. To overcome these limitations, we propose a linear
classification algorithm with a majority constraint and prove that it is
strictly more resilient than the traditional algorithms. Evaluations on both
synthetic data and a real-world retrospective arrhythmia medical case-study
show that the traditional algorithms are vulnerable to tampered training data,
whereas the proposed algorithm is more resilient (as measured by worst-case
tampering).
The Trimmed Lasso: Sparsity and Robustness
Authors: Dimitris Bertsimas, Martin S. Copenhaver, Rahul Mazumder
Date: 2017-08-15 00:00:00

Nonconvex penalty methods for sparse modeling in linear regression have been
a topic of fervent interest in recent years. Herein, we study a family of
nonconvex penalty functions that we call the trimmed Lasso and that offers
exact control over the desired level of sparsity of estimators. We analyze its
structural properties and in doing so show the following:
  1) Drawing parallels between robust statistics and robust optimization, we
show that the trimmed-Lasso-regularized least squares problem can be viewed as
a generalized form of total least squares under a specific model of
uncertainty. In contrast, this same model of uncertainty, viewed instead
through a robust optimization lens, leads to the convex SLOPE (or OWL) penalty.
  2) Further, in relating the trimmed Lasso to commonly used sparsity-inducing
penalty functions, we provide a succinct characterization of the connection
between trimmed-Lasso- like approaches and penalty functions that are
coordinate-wise separable, showing that the trimmed penalties subsume existing
coordinate-wise separable penalties, with strict containment in general.
  3) Finally, we describe a variety of exact and heuristic algorithms, both
existing and new, for trimmed Lasso regularized estimation problems. We include
a comparison between the different approaches and an accompanying
implementation of the algorithms.
DesnowNet: Context-Aware Deep Network for Snow Removal
Authors: Yun-Fu Liu, Da-Wei Jaw, Shih-Chia Huang, Jenq-Neng Hwang
Date: 2017-08-15 00:00:00
Tasks: Diversity, Semantic Segmentation, Single Image Desnowing, Snow Removal

Existing learning-based atmospheric particle-removal approaches such as those
used for rainy and hazy images are designed with strong assumptions regarding
spatial frequency, trajectory, and translucency. However, the removal of snow
particles is more complicated because it possess the additional attributes of
particle size and shape, and these attributes may vary within a single image.
Currently, hand-crafted features are still the mainstream for snow removal,
making significant generalization difficult to achieve. In response, we have
designed a multistage network codenamed DesnowNet to in turn deal with the
removal of translucent and opaque snow particles. We also differentiate snow
into attributes of translucency and chromatic aberration for accurate
estimation. Moreover, our approach individually estimates residual complements
of the snow-free images to recover details obscured by opaque snow.
Additionally, a multi-scale design is utilized throughout the entire network to
model the diversity of snow. As demonstrated in experimental results, our
approach outperforms state-of-the-art learning-based atmospheric phenomena
removal methods and one semantic segmentation baseline on the proposed Snow100K
dataset in both qualitative and quantitative comparisons. The results indicate
our network would benefit applications involving computer vision and graphics.
A Fixed-Point of View on Gradient Methods for Big Data
Authors: Alexander Jung
Date: 2017-06-29 00:00:00

Interpreting gradient methods as fixed-point iterations, we provide a
detailed analysis of those methods for minimizing convex objective functions.
Due to their conceptual and algorithmic simplicity, gradient methods are widely
used in machine learning for massive data sets (big data). In particular,
stochastic gradient methods are considered the de- facto standard for training
deep neural networks. Studying gradient methods within the realm of fixed-point
theory provides us with powerful tools to analyze their convergence properties.
In particular, gradient methods using inexact or noisy gradients, such as
stochastic gradient descent, can be studied conveniently using well-known
results on inexact fixed-point iterations. Moreover, as we demonstrate in this
paper, the fixed-point approach allows an elegant derivation of accelerations
for basic gradient methods. In particular, we will show how gradient descent
can be accelerated by a fixed-point preserving transformation of an operator
associated with the objective function.
Vision-Based Fallen Person Detection for the Elderly
Authors: Markus D. Solbach, John K. Tsotsos
Date: 2017-07-24 00:00:00
Tasks: Human Detection

Falls are serious and costly for elderly people. The Centers for Disease
Control and Prevention of the US reports that millions of older people, 65 and
older, fall each year at least once. Serious injuries such as; hip fractures,
broken bones or head injury, are caused by 20% of the falls. The time it takes
to respond and treat a fallen person is crucial. With this paper we present a
new , non-invasive system for fallen people detection. Our approach uses only
stereo camera data for passively sensing the environment. The key novelty is a
human fall detector which uses a CNN based human pose estimator in combination
with stereo data to reconstruct the human pose in 3D and estimate the ground
plane in 3D. Furthermore, our system consists of a reasoning module which
formulates a number of measures to reason whether a person is fallen. We have
tested our approach in different scenarios covering most activities elderly
people might encounter living at home. Based on our extensive evaluations, our
systems shows high accuracy and almost no miss-classification. To reproduce our
results, the implementation is publicly available to the scientific community.
Cognitive Subscore Trajectory Prediction in Alzheimer's Disease
Authors: Lev E. Givon, Laura J. Mariano, David O'Dowd, John M. Irvine, Abraham R. Schneider
Date: 2017-06-26 00:00:00
Tasks: Diagnostic, feature selection, Prediction, Trajectory Prediction

Accurate diagnosis of Alzheimer's Disease (AD) entails clinical evaluation of
multiple cognition metrics and biomarkers. Metrics such as the Alzheimer's
Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple
subscores that quantify different aspects of a patient's cognitive state such
as learning, memory, and language production/comprehension. Although
computer-aided diagnostic techniques for classification of a patient's current
disease state exist, they provide little insight into the relationship between
changes in brain structure and different aspects of a patient's cognitive state
that occur over time in AD. We have developed a Convolutional Neural Network
architecture that can concurrently predict the trajectories of the 13 subscores
comprised by a subject's ADAS-cog examination results from a current minimally
preprocessed structural MRI scan up to 36 months from image acquisition time
without resorting to manual feature extraction. Mean performance metrics are
within range of those of existing techniques that require manual feature
selection and are limited to predicting aggregate scores.
Pathological Pulmonary Lobe Segmentation from CT Images using Progressive Holistically Nested Neural Networks and Random Walker
Authors: Kevin George, Adam P. Harrison, Dakai Jin, Ziyue Xu, Daniel J. Mollura
Date: 2017-08-15 00:00:00

Automatic pathological pulmonary lobe segmentation(PPLS) enables regional
analyses of lung disease, a clinically important capability. Due to often
incomplete lobe boundaries, PPLS is difficult even for experts, and most prior
art requires inference from contextual information. To address this, we propose
a novel PPLS method that couples deep learning with the random walker (RW)
algorithm. We first employ the recent progressive holistically-nested network
(P-HNN) model to identify potential lobar boundaries, then generate final
segmentations using a RW that is seeded and weighted by the P-HNN output. We
are the first to apply deep learning to PPLS. The advantages are independence
from prior airway/vessel segmentations, increased robustness in diseased lungs,
and methodological simplicity that does not sacrifice accuracy. Our method
posts a high mean Jaccard score of 0.888$\pm$0.164 on a held-out set of 154 CT
scans from lung-disease patients, while also significantly (p < 0.001)
outperforming a state-of-the-art method.