pwc-archive/papers-with-abstracts huggingface dataset:
Generating Extractive Summaries of Scientific Paradigms
Authors: Vahed Qazvinian, Dragomir R. Radev, Saif M. Mohammad, Bonnie Dorr, David Zajic, Michael Whidby, Taesun Moon
Date: 2014-02-04 00:00:00
Tasks: Articles, Community Detection, Dependency Parsing, Position, Question Answering

Researchers and scientists increasingly find themselves in the position of
having to quickly understand large amounts of technical material. Our goal is
to effectively serve this need by using bibliometric text mining and
summarization techniques to generate summaries of scientific literature. We
show how we can use citations to produce automatically generated, readily
consumable, technical extractive summaries. We first propose C-LexRank, a model
for summarizing single scientific articles based on citations, which employs
community detection and extracts salient information-rich sentences. Next, we
further extend our experiments to summarize a set of papers, which cover the
same scientific topic. We generate extractive summaries of a set of Question
Answering (QA) and Dependency Parsing (DP) papers, their abstracts, and their
citation sentences and show that citations have unique information amenable to
creating a summary.
How Does Latent Semantic Analysis Work? A Visualisation Approach
Authors: Jan Koeman, William Rea
Date: 2014-02-03 00:00:00

By using a small example, an analogy to photographic compression, and a
simple visualization using heatmaps, we show that latent semantic analysis
(LSA) is able to extract what appears to be semantic meaning of words from a
set of documents by blurring the distinctions between the words.
Applying Supervised Learning Algorithms and a New Feature Selection Method to Predict Coronary Artery Disease
Authors: Hubert Haoyang Duan
Date: 2014-02-03 00:00:00
Tasks: Dimensionality Reduction, feature selection, LEMMA

From a fresh data science perspective, this thesis discusses the prediction
of coronary artery disease based on genetic variations at the DNA base pair
level, called Single-Nucleotide Polymorphisms (SNPs), collected from the
Ontario Heart Genomics Study (OHGS).
  First, the thesis explains two commonly used supervised learning algorithms,
the k-Nearest Neighbour (k-NN) and Random Forest classifiers, and includes a
complete proof that the k-NN classifier is universally consistent in any finite
dimensional normed vector space. Second, the thesis introduces two
dimensionality reduction steps, Random Projections, a known feature extraction
technique based on the Johnson-Lindenstrauss lemma, and a new method termed
Mass Transportation Distance (MTD) Feature Selection for discrete domains.
Then, this thesis compares the performance of Random Projections with the k-NN
classifier against MTD Feature Selection and Random Forest, for predicting
artery disease based on accuracy, the F-Measure, and area under the Receiver
Operating Characteristic (ROC) curve.
  The comparative results demonstrate that MTD Feature Selection with Random
Forest is vastly superior to Random Projections and k-NN. The Random Forest
classifier is able to obtain an accuracy of 0.6660 and an area under the ROC
curve of 0.8562 on the OHGS genetic dataset, when 3335 SNPs are selected by MTD
Feature Selection for classification. This area is considerably better than the
previous high score of 0.608 obtained by Davies et al. in 2010 on the same
dataset.
A Lower Bound for the Variance of Estimators for Nakagami m Distribution
Authors: Rangeet Mitra, Amit Kumar Mishra, Tarun Choubisa
Date: 2014-02-03 00:00:00

Recently, we have proposed a maximum likelihood iterative algorithm for
estimation of the parameters of the Nakagami-m distribution. This technique
performs better than state of art estimation techniques for this distribution.
This could be of particular use in low data or block based estimation problems.
In these scenarios, the estimator should be able to give accurate estimates in
the mean square sense with less amounts of data. Also, the estimates should
improve with the increase in number of blocks received. In this paper, we see
through our simulations, that our proposal is well designed for such
requirements. Further, it is well known in the literature that an efficient
estimator does not exist for Nakagami-m distribution. In this paper, we derive
a theoretical expression for the variance of our proposed estimator. We find
that this expression clearly fits the experimental curve for the variance of
the proposed estimator. This expression is pretty close to the cramer-rao lower
bound(CRLB).
A high-reproducibility and high-accuracy method for automated topic classification
Authors: Andrea Lancichinetti, M. Irmak Sirer, Jane. X. Wang, Daniel Acuna, Konrad Körding, Luís A. Nunes Amaral
Date: 2014-02-03 00:00:00
Tasks: Community Detection, Computational Efficiency, General Classification, Topic Classification, Vocal Bursts Intensity Prediction

Much of human knowledge sits in large databases of unstructured text.
Leveraging this knowledge requires algorithms that extract and record metadata
on unstructured text documents. Assigning topics to documents will enable
intelligent search, statistical characterization, and meaningful
classification. Latent Dirichlet allocation (LDA) is the state-of-the-art in
topic classification. Here, we perform a systematic theoretical and numerical
analysis that demonstrates that current optimization techniques for LDA often
yield results which are not accurate in inferring the most suitable model
parameters. Adapting approaches for community detection in networks, we propose
a new algorithm which displays high-reproducibility and high-accuracy, and also
has high computational efficiency. We apply it to a large set of documents in
the English Wikipedia and reveal its hierarchical structure. Our algorithm
promises to make "big data" text analysis systems more reliable.
Multidiscipinary Optimization For Gas Turbines Design
Authors: Francesco Bertini, Lorenzo Dal Mas, Luca Vassio, Enrico Ampellio
Date: 2014-02-03 00:00:00

State-of-the-art aeronautic Low Pressure gas Turbines (LPTs) are already
characterized by high quality standards, thus they offer very narrow margins of
improvement. Typical design process starts with a Concept Design (CD) phase,
defined using mean-line 1D and other low-order tools, and evolves through a
Preliminary Design (PD) phase, which allows the geometric definition in
details. In this framework, multidisciplinary optimization is the only way to
properly handle the complicated peculiarities of the design. The authors
present different strategies and algorithms that have been implemented
exploiting the PD phase as a real-like design benchmark to illustrate results.
The purpose of this work is to describe the optimization techniques, their
settings and how to implement them effectively in a multidisciplinary
environment. Starting from a basic gradient method and a semi-random second
order method, the authors have introduced an Artificial Bee Colony-like
optimizer, a multi-objective Genetic Diversity Evolutionary Algorithm [1] and a
multi-objective response surface approach based on Artificial Neural Network,
parallelizing and customizing them for the gas turbine study. Moreover, speedup
and improvement arrangements are embedded in different hybrid strategies with
the aim at finding the best solutions for different kind of problems that arise
in this field.
Associative Memories Based on Multiple-Valued Sparse Clustered Networks
Authors: Hooman Jarollahi, Naoya Onizawa, Takahiro Hanyu, Warren J. Gross
Date: 2014-02-03 00:00:00
Tasks: Retrieval

Associative memories are structures that store data patterns and retrieve
them given partial inputs. Sparse Clustered Networks (SCNs) are
recently-introduced binary-weighted associative memories that significantly
improve the storage and retrieval capabilities over the prior state-of-the art.
However, deleting or updating the data patterns result in a significant
increase in the data retrieval error probability. In this paper, we propose an
algorithm to address this problem by incorporating multiple-valued weights for
the interconnections used in the network. The proposed algorithm lowers the
error rate by an order of magnitude for our sample network with 60% deleted
contents. We then investigate the advantages of the proposed algorithm for
hardware implementations.
Thompson Sampling for Contextual Bandits with Linear Payoffs
Authors: Shipra Agrawal, Navin Goyal
Date: 2012-09-15 00:00:00
Tasks: Multi-Armed Bandits, Thompson Sampling

Thompson Sampling is one of the oldest heuristics for multi-armed bandit
problems. It is a randomized algorithm based on Bayesian ideas, and has
recently generated significant interest after several studies demonstrated it
to have better empirical performance compared to the state-of-the-art methods.
However, many questions regarding its theoretical performance remained open. In
this paper, we design and analyze a generalization of Thompson Sampling
algorithm for the stochastic contextual multi-armed bandit problem with linear
payoff functions, when the contexts are provided by an adaptive adversary. This
is among the most important and widely studied versions of the contextual
bandits problem. We provide the first theoretical guarantees for the contextual
version of Thompson Sampling. We prove a high probability regret bound of
$\tilde{O}(d^{3/2}\sqrt{T})$ (or $\tilde{O}(d\sqrt{T \log(N)})$), which is the
best regret bound achieved by any computationally efficient algorithm available
for this problem in the current literature, and is within a factor of
$\sqrt{d}$ (or $\sqrt{\log(N)}$) of the information-theoretic lower bound for
this problem.
Learning to Optimize Via Posterior Sampling
Authors: Daniel Russo, Benjamin Van Roy
Date: 2013-01-11 00:00:00
Tasks: Thompson Sampling

This paper considers the use of a simple posterior sampling algorithm to
balance between exploration and exploitation when learning to optimize actions
such as in multi-armed bandit problems. The algorithm, also known as Thompson
Sampling, offers significant advantages over the popular upper confidence bound
(UCB) approach, and can be applied to problems with finite or infinite action
spaces and complicated relationships among action rewards. We make two
theoretical contributions. The first establishes a connection between posterior
sampling and UCB algorithms. This result lets us convert regret bounds
developed for UCB algorithms into Bayesian regret bounds for posterior
sampling. Our second theoretical contribution is a Bayesian regret bound for
posterior sampling that applies broadly and can be specialized to many model
classes. This bound depends on a new notion we refer to as the eluder
dimension, which measures the degree of dependence among action rewards.
Compared to UCB algorithm Bayesian regret bounds for specific model classes,
our general bound matches the best available for linear models and is stronger
than the best available for generalized linear models. Further, our analysis
provides insight into performance advantages of posterior sampling, which are
highlighted through simulation results that demonstrate performance surpassing
recently proposed UCB algorithms.
A Robust Framework for Moving-Object Detection and Vehicular Traffic Density Estimation
Authors: Pranam Janney, Glenn Geers
Date: 2014-02-03 00:00:00
Tasks: Density Estimation, Moving Object Detection, Object, object-detection, Object Detection

Intelligent machines require basic information such as moving-object
detection from videos in order to deduce higher-level semantic information. In
this paper, we propose a methodology that uses a texture measure to detect
moving objects in video. The methodology is computationally inexpensive,
requires minimal parameter fine-tuning and also is resilient to noise,
illumination changes, dynamic background and low frame rate. Experimental
results show that performance of the proposed approach is higher than those of
state-of-the-art approaches. We also present a framework for vehicular traffic
density estimation using the foreground object detection technique and present
a comparison between the foreground object detection-based framework and the
classical density state modelling-based framework for vehicular traffic density
estimation.
Transductive Learning with Multi-class Volume Approximation
Authors: Gang Niu, Bo Dai, Marthinus Christoffel du Plessis, Masashi Sugiyama
Date: 2014-02-03 00:00:00
Tasks: Transductive Learning

Given a hypothesis space, the large volume principle by Vladimir Vapnik
prioritizes equivalence classes according to their volume in the hypothesis
space. The volume approximation has hitherto been successfully applied to
binary learning problems. In this paper, we extend it naturally to a more
general definition which can be applied to several transductive problem
settings, such as multi-class, multi-label and serendipitous learning. Even
though the resultant learning method involves a non-convex optimization
problem, the globally optimal solution is almost surely unique and can be
obtained in O(n^3) time. We theoretically provide stability and error analyses
for the proposed method, and then experimentally show that it is promising.
Principled Graph Matching Algorithms for Integrating Multiple Data Sources
Authors: Duo Zhang, Benjamin I. P. Rubinstein, Jim Gemmell
Date: 2014-02-03 00:00:00
Tasks: Combinatorial Optimization, Data Integration, Entity Resolution, Graph Matching

This paper explores combinatorial optimization for problems of max-weight
graph matching on multi-partite graphs, which arise in integrating multiple
data sources. Entity resolution-the data integration problem of performing
noisy joins on structured data-typically proceeds by first hashing each record
into zero or more blocks, scoring pairs of records that are co-blocked for
similarity, and then matching pairs of sufficient similarity. In the most
common case of matching two sources, it is often desirable for the final
matching to be one-to-one (a record may be matched with at most one other);
members of the database and statistical record linkage communities accomplish
such matchings in the final stage by weighted bipartite graph matching on
similarity scores. Such matchings are intuitively appealing: they leverage a
natural global property of many real-world entity stores-that of being nearly
deduped-and are known to provide significant improvements to precision and
recall. Unfortunately unlike the bipartite case, exact max-weight matching on
multi-partite graphs is known to be NP-hard. Our two-fold algorithmic
contributions approximate multi-partite max-weight matching: our first
algorithm borrows optimization techniques common to Bayesian probabilistic
inference; our second is a greedy approximation algorithm. In addition to a
theoretical guarantee on the latter, we present comparisons on a real-world ER
problem from Bing significantly larger than typically found in the literature,
publication data, and on a series of synthetic problems. Our results quantify
significant improvements due to exploiting multiple sources, which are made
possible by global one-to-one constraints linking otherwise independent
matching sub-problems. We also discover that our algorithms are complementary:
one being much more robust under noise, and the other being simple to implement
and very fast to run.
Recovery guarantees for exemplar-based clustering
Authors: Abhinav Nellore, Rachel Ward
Date: 2013-09-12 00:00:00
Tasks: Clustering

For a certain class of distributions, we prove that the linear programming
relaxation of $k$-medoids clustering---a variant of $k$-means clustering where
means are replaced by exemplars from within the dataset---distinguishes points
drawn from nonoverlapping balls with high probability once the number of points
drawn and the separation distance between any two balls are sufficiently large.
Our results hold in the nontrivial regime where the separation distance is
small enough that points drawn from different balls may be closer to each other
than points drawn from the same ball; in this case, clustering by thresholding
pairwise distances between points can fail. We also exhibit numerical evidence
of high-probability recovery in a substantially more permissive regime.
Learning Information Spread in Content Networks
Authors: Cédric Lagnier, Simon Bourigault, Sylvain Lamprier, Ludovic Denoyer, Patrick Gallinari
Date: 2013-12-20 00:00:00

We introduce a model for predicting the diffusion of content information on
social media. When propagation is usually modeled on discrete graph structures,
we introduce here a continuous diffusion model, where nodes in a diffusion
cascade are projected onto a latent space with the property that their
proximity in this space reflects the temporal diffusion process. We focus on
the task of predicting contaminated users for an initial initial information
source and provide preliminary results on differents datasets.
Collaborative Receptive Field Learning
Authors: Shu Kong, Zhuolin Jiang, Qiang Yang
Date: 2014-02-02 00:00:00
Tasks: General Classification, Object Categorization

The challenge of object categorization in images is largely due to arbitrary
translations and scales of the foreground objects. To attack this difficulty,
we propose a new approach called collaborative receptive field learning to
extract specific receptive fields (RF's) or regions from multiple images, and
the selected RF's are supposed to focus on the foreground objects of a common
category. To this end, we solve the problem by maximizing a submodular function
over a similarity graph constructed by a pool of RF candidates. However,
measuring pairwise distance of RF's for building the similarity graph is a
nontrivial problem. Hence, we introduce a similarity metric called
pyramid-error distance (PED) to measure their pairwise distances through
summing up pyramid-like matching errors over a set of low-level features.
Besides, in consistent with the proposed PED, we construct a simple
nonparametric classifier for classification. Experimental results show that our
method effectively discovers the foreground objects in images, and improves
classification performance.
Extension of Sparse Randomized Kaczmarz Algorithm for Multiple Measurement Vectors
Authors: Hemant Kumar Aggarwal, Angshul Majumdar
Date: 2014-01-10 00:00:00
Tasks: Face Recognition, Fairness

The Kaczmarz algorithm is popular for iteratively solving an overdetermined
system of linear equations. The traditional Kaczmarz algorithm can approximate
the solution in few sweeps through the equations but a randomized version of
the Kaczmarz algorithm was shown to converge exponentially and independent of
number of equations. Recently an algorithm for finding sparse solution to a
linear system of equations has been proposed based on weighted randomized
Kaczmarz algorithm. These algorithms solves single measurement vector problem;
however there are applications were multiple-measurements are available. In
this work, the objective is to solve a multiple measurement vector problem with
common sparse support by modifying the randomized Kaczmarz algorithm. We have
also modeled the problem of face recognition from video as the multiple
measurement vector problem and solved using our proposed technique. We have
compared the proposed algorithm with state-of-art spectral projected gradient
algorithm for multiple measurement vectors on both real and synthetic datasets.
The Monte Carlo simulations confirms that our proposed algorithm have better
recovery and convergence rate than the MMV version of spectral projected
gradient algorithm under fairness constraints.
Video Compressive Sensing for Dynamic MRI
Authors: Jianing V. Shi, Wotao Yin, Aswin C. Sankaranarayanan, Richard G. Baraniuk
Date: 2014-01-30 00:00:00
Tasks: Compressive Sensing, Video Compressive Sensing

We present a video compressive sensing framework, termed kt-CSLDS, to
accelerate the image acquisition process of dynamic magnetic resonance imaging
(MRI). We are inspired by a state-of-the-art model for video compressive
sensing that utilizes a linear dynamical system (LDS) to model the motion
manifold. Given compressive measurements, the state sequence of an LDS can be
first estimated using system identification techniques. We then reconstruct the
observation matrix using a joint structured sparsity assumption. In particular,
we minimize an objective function with a mixture of wavelet sparsity and joint
sparsity within the observation matrix. We derive an efficient convex
optimization algorithm through alternating direction method of multipliers
(ADMM), and provide a theoretical guarantee for global convergence. We
demonstrate the performance of our approach for video compressive sensing, in
terms of reconstruction accuracy. We also investigate the impact of various
sampling strategies. We apply this framework to accelerate the acquisition
process of dynamic MRI and show it achieves the best reconstruction accuracy
with the least computational time compared with existing algorithms in the
literature.
What Is It Like to Be a Brain Simulation?
Authors: Eray Özkural
Date: 2014-02-01 00:00:00

We frame the question of what kind of subjective experience a brain
simulation would have in contrast to a biological brain. We discuss the brain
prosthesis thought experiment. We evaluate how the experience of the brain
simulation might differ from the biological, according to a number of
hypotheses about experience and the properties of simulation. Then, we identify
finer questions relating to the original inquiry, and answer them from both a
general physicalist, and panexperientialist perspective.
Dual-to-kernel learning with ideals
Authors: Franz J. Király, Martin Kreuzer, Louis Theran
Date: 2014-02-01 00:00:00

In this paper, we propose a theory which unifies kernel learning and symbolic
algebraic methods. We show that both worlds are inherently dual to each other,
and we use this duality to combine the structure-awareness of algebraic methods
with the efficiency and generality of kernels. The main idea lies in relating
polynomial rings to feature space, and ideals to manifolds, then exploiting
this generative-discriminative duality on kernel matrices. We illustrate this
by proposing two algorithms, IPCA and AVICA, for simultaneous manifold and
feature learning, and test their accuracy on synthetic and real world data.
DinTucker: Scaling up Gaussian process models on multidimensional arrays with billions of elements
Authors: Shandian Zhe, Yuan Qi, Youngja Park, Ian Molloy, Suresh Chari
Date: 2013-11-12 00:00:00
Tasks: Tensor Decomposition, Variational Inference

Infinite Tucker Decomposition (InfTucker) and random function prior models,
as nonparametric Bayesian models on infinite exchangeable arrays, are more
powerful models than widely-used multilinear factorization methods including
Tucker and PARAFAC decomposition, (partly) due to their capability of modeling
nonlinear relationships between array elements. Despite their great predictive
performance and sound theoretical foundations, they cannot handle massive data
due to a prohibitively high training time. To overcome this limitation, we
present Distributed Infinite Tucker (DINTUCKER), a large-scale nonlinear tensor
decomposition algorithm on MAPREDUCE. While maintaining the predictive accuracy
of InfTucker, it is scalable on massive data. DINTUCKER is based on a new
hierarchical Bayesian model that enables local training of InfTucker on
subarrays and information integration from all local training results. We use
distributed stochastic gradient descent, coupled with variational inference, to
train this model. We apply DINTUCKER to multidimensional arrays with billions
of elements from applications in the "Read the Web" project (Carlson et al.,
2010) and in information security and compare it with the state-of-the-art
large-scale tensor decomposition method, GigaTensor. On both datasets,
DINTUCKER achieves significantly higher prediction accuracy with less
computational time.