pwc-archive/papers-with-abstracts huggingface dataset:
On the Ground Validation of Online Diagnosis with Twitter and Medical Records
Authors: Todd Bodnar, Victoria C Barclay, Nilam Ram, Conrad S Tucker, Marcel Salathé
Date: 2014-04-11 00:00:00
Tasks: Anomaly Detection

Social media has been considered as a data source for tracking disease.
However, most analyses are based on models that prioritize strong correlation
with population-level disease rates over determining whether or not specific
individual users are actually sick. Taking a different approach, we develop a
novel system for social-media based disease detection at the individual level
using a sample of professionally diagnosed individuals. Specifically, we
develop a system for making an accurate influenza diagnosis based on an
individual's publicly available Twitter data. We find that about half (17/35 =
48.57%) of the users in our sample that were sick explicitly discuss their
disease on Twitter. By developing a meta classifier that combines text
analysis, anomaly detection, and social network analysis, we are able to
diagnose an individual with greater than 99% accuracy even if she does not
discuss her health.
A Reverse Hierarchy Model for Predicting Eye Fixations
Authors: Tianlin Shi, Liang Ming, Xiaolin Hu
Date: 2014-04-11 00:00:00
Proceeding: CVPR 2014 6
Tasks: Image Super-Resolution, Saliency Detection, Super-Resolution

A number of psychological and physiological evidences suggest that early
visual attention works in a coarse-to-fine way, which lays a basis for the
reverse hierarchy theory (RHT). This theory states that attention propagates
from the top level of the visual hierarchy that processes gist and abstract
information of input, to the bottom level that processes local details.
Inspired by the theory, we develop a computational model for saliency detection
in images. First, the original image is downsampled to different scales to
constitute a pyramid. Then, saliency on each layer is obtained by image
super-resolution reconstruction from the layer above, which is defined as
unpredictability from this coarse-to-fine reconstruction. Finally, saliency on
each layer of the pyramid is fused into stochastic fixations through a
probabilistic model, where attention initiates from the top layer and
propagates downward through the pyramid. Extensive experiments on two standard
eye-tracking datasets show that the proposed method can achieve competitive
results with state-of-the-art models.
Automatic Detection of Reuses and Citations in Literary Texts
Authors: Jean-Gabriel Ganascia, Pierre Glaudes, Andrea Del Lungo
Date: 2014-04-11 00:00:00

For more than forty years now, modern theories of literature (Compagnon,
1979) insist on the role of paraphrases, rewritings, citations, reciprocal
borrowings and mutual contributions of any kinds. The notions of
intertextuality, transtextuality, hypertextuality/hypotextuality, were
introduced in the seventies and eighties to approach these phenomena. The
careful analysis of these references is of particular interest in evaluating
the distance that the creator voluntarily introduces with his/her masters.
Phoebus is collaborative project that makes computer scientists from the
University Pierre and Marie Curie (LIP6-UPMC) collaborate with the literary
teams of Paris-Sorbonne University with the aim to develop efficient tools for
literary studies that take advantage of modern computer science techniques. In
this context, we have developed a piece of software that automatically detects
and explores networks of textual reuses in classical literature. This paper
describes the principles on which is based this program, the significant
results that have already been obtained and the perspectives for the near
future.
Supersparse Linear Integer Models for Interpretable Classification
Authors: Berk Ustun, Stefano Tracà, Cynthia Rudin
Date: 2013-06-27 00:00:00
Tasks: Classification, General Classification

Scoring systems are classification models that only require users to add,
subtract and multiply a few meaningful numbers to make a prediction. These
models are often used because they are practical and interpretable. In this
paper, we introduce an off-the-shelf tool to create scoring systems that both
accurate and interpretable, known as a Supersparse Linear Integer Model (SLIM).
SLIM is a discrete optimization problem that minimizes the 0-1 loss to
encourage a high level of accuracy, regularizes the L0-norm to encourage a high
level of sparsity, and constrains coefficients to a set of interpretable
values. We illustrate the practical and interpretable nature of SLIM scoring
systems through applications in medicine and criminology, and show that they
are are accurate and sparse in comparison to state-of-the-art classification
models using numerical experiments.
A Tutorial on Independent Component Analysis
Authors: Jonathon Shlens
Date: 2014-04-11 00:00:00
Tasks: BIG-bench Machine Learning

Independent component analysis (ICA) has become a standard data analysis
technique applied to an array of problems in signal processing and machine
learning. This tutorial provides an introduction to ICA based on linear algebra
formulating an intuition for ICA from first principles. The goal of this
tutorial is to provide a solid foundation on this advanced topic so that one
might learn the motivation behind ICA, learn why and when to apply this
technique and in the process gain an introduction to this exciting field of
active research.
Targeting HIV-related Medication Side Effects and Sentiment Using Twitter Data
Authors: Cosme Adrover, Todd Bodnar, Marcel Salathe
Date: 2014-04-11 00:00:00
Tasks: Descriptive

We present a descriptive analysis of Twitter data. Our study focuses on
extracting the main side effects associated with HIV treatments. The crux of
our work was the identification of personal tweets referring to HIV. We
summarize our results in an infographic aimed at the general public. In
addition, we present a measure of user sentiment based on hand-rated tweets.
Distribution-Aware Sampling and Weighted Model Counting for SAT
Authors: Supratik Chakraborty, Daniel J. Fremont, Kuldeep S. Meel, Sanjit A. Seshia, Moshe Y. Vardi
Date: 2014-04-11 00:00:00
Tasks: Computational Efficiency

Given a CNF formula and a weight for each assignment of values to variables,
two natural problems are weighted model counting and distribution-aware
sampling of satisfying assignments. Both problems have a wide variety of
important applications. Due to the inherent complexity of the exact versions of
the problems, interest has focused on solving them approximately. Prior work in
this area scaled only to small problems in practice, or failed to provide
strong theoretical guarantees, or employed a computationally-expensive maximum
a posteriori probability (MAP) oracle that assumes prior knowledge of a
factored representation of the weight distribution. We present a novel approach
that works with a black-box oracle for weights of assignments and requires only
an {\NP}-oracle (in practice, a SAT-solver) to solve both the counting and
sampling problems. Our approach works under mild assumptions on the
distribution of weights of satisfying assignments, provides strong theoretical
guarantees, and scales to problems involving several thousand variables. We
also show that the assumptions can be significantly relaxed while improving
computational efficiency if a factored representation of the weights is known.
Multi-agent Inverse Reinforcement Learning for Two-person Zero-sum Games
Authors: Xiaomin Lin, Peter A. Beling, Randy Cogill
Date: 2014-03-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Vocal Bursts Valence Prediction

The focus of this paper is a Bayesian framework for solving a class of problems termed multi-agent inverse reinforcement learning (MIRL). Compared to the well-known inverse reinforcement learning (IRL) problem, MIRL is formalized in the context of stochastic games, which generalize Markov decision processes to game theoretic scenarios. We establish a theoretical foundation for competitive two-agent zero-sum MIRL problems and propose a Bayesian solution approach in which the generative model is based on an assumption that the two agents follow a minimax bi-policy. Numerical results are presented comparing the Bayesian MIRL method with two existing methods in the context of an abstract soccer game. Investigation centers on relationships between the extent of prior information and the quality of learned rewards. Results suggest that covariance structure is more important than mean value in reward priors.
Gradient-based Laplacian Feature Selection
Authors: Bo Wang, Anna Goldenberg
Date: 2014-04-10 00:00:00
Tasks: feature selection, Object Recognition

Analysis of high dimensional noisy data is of essence across a variety of
research fields. Feature selection techniques are designed to find the relevant
feature subset that can facilitate classification or pattern detection.
Traditional (supervised) feature selection methods utilize label information to
guide the identification of relevant feature subsets. In this paper, however,
we consider the unsupervised feature selection problem. Without the label
information, it is particularly difficult to identify a small set of relevant
features due to the noisy nature of real-world data which corrupts the
intrinsic structure of the data. Our Gradient-based Laplacian Feature Selection
(GLFS) selects important features by minimizing the variance of the Laplacian
regularized least squares regression model. With $\ell_1$ relaxation, GLFS can
find a sparse subset of features that is relevant to the Laplacian manifolds.
Extensive experiments on simulated, three real-world object recognition and two
computational biology datasets, have illustrated the power and superior
performance of our approach over multiple state-of-the-art unsupervised feature
selection methods. Additionally, we show that GLFS selects a sparser set of
more relevant features in a supervised setting outperforming the popular
elastic net methodology.
Clustering and Relational Ambiguity: from Text Data to Natural Data
Authors: Nicolas Turenne
Date: 2013-11-21 00:00:00
Tasks: Clustering

Text data is often seen as "take-away" materials with little noise and easy
to process information. Main questions are how to get data and transform them
into a good document format. But data can be sensitive to noise oftenly called
ambiguities. Ambiguities are aware from a long time, mainly because polysemy is
obvious in language and context is required to remove uncertainty. I claim in
this paper that syntactic context is not suffisant to improve interpretation.
In this paper I try to explain that firstly noise can come from natural data
themselves, even involving high technology, secondly texts, seen as verified
but meaningless, can spoil content of a corpus; it may lead to contradictions
and background noise.
Overview of Stemming Algorithms for Indian and Non-Indian Languages
Authors: Dalwadi Bijal, Suthar Sanket
Date: 2014-04-10 00:00:00
Tasks: Form, Information Retrieval, Retrieval

Stemming is a pre-processing step in Text Mining applications as well as a
very common requirement of Natural Language processing functions. Stemming is
the process for reducing inflected words to their stem. The main purpose of
stemming is to reduce different grammatical forms / word forms of a word like
its noun, adjective, verb, adverb etc. to its root form. Stemming is widely
uses in Information Retrieval system and reduces the size of index files. We
can say that the goal of stemming is to reduce inflectional forms and sometimes
derivationally related forms of a word to a common base form. In this paper we
have discussed different stemming algorithm for non-Indian and Indian language,
methods of stemming, accuracy and errors.
Auto-Encoding Variational Bayes
Authors: Diederik P. Kingma, Max Welling
Date: 2013-12-20 00:00:00
Tasks: Anomaly Detection, Image Clustering, Variational Inference

How can we perform efficient inference and learning in directed probabilistic
models, in the presence of continuous latent variables with intractable
posterior distributions, and large datasets? We introduce a stochastic
variational inference and learning algorithm that scales to large datasets and,
under some mild differentiability conditions, even works in the intractable
case. Our contributions is two-fold. First, we show that a reparameterization
of the variational lower bound yields a lower bound estimator that can be
straightforwardly optimized using standard stochastic gradient methods. Second,
we show that for i.i.d. datasets with continuous latent variables per
datapoint, posterior inference can be made especially efficient by fitting an
approximate inference model (also called a recognition model) to the
intractable posterior using the proposed lower bound estimator. Theoretical
advantages are reflected in experimental results.
Real Time Speckle Image De-Noising
Authors: D. Sachin Kumar, P. R. Seshadri, N. Vaishnav, Dr. Saraswathi Janaki
Date: 2014-04-10 00:00:00

The paper presents real time speckle de-noising based on activity computation
algorithm and wavelet transform. Speckles arise in an image when laser light is
reflected from an illuminated surface. The process involves detection of
speckles in an image by obtaining a number of frames of the same object under
different illumination or angle and comparing the frames for the granular
computation and de-noising the same on presence of greater activity index. The
project can be implemented in FPGA (Field Programmable Gate Array) technology.
The results can be shown that the used activity computation algorithm and
wavelet transform has better accuracy in the process of speckle detection and
de-noising.
A New Clustering Approach for Anomaly Intrusion Detection
Authors: Ravi Ranjan, G. Sahoo
Date: 2014-04-10 00:00:00
Tasks: Clustering, Intrusion Detection

Recent advances in technology have made our work easier compare to earlier
times. Computer network is growing day by day but while discussing about the
security of computers and networks it has always been a major concerns for
organizations varying from smaller to larger enterprises. It is true that
organizations are aware of the possible threats and attacks so they always
prepare for the safer side but due to some loopholes attackers are able to make
attacks. Intrusion detection is one of the major fields of research and
researchers are trying to find new algorithms for detecting intrusions.
Clustering techniques of data mining is an interested area of research for
detecting possible intrusions and attacks. This paper presents a new clustering
approach for anomaly intrusion detection by using the approach of K-medoids
method of clustering and its certain modifications. The proposed algorithm is
able to achieve high detection rate and overcomes the disadvantages of K-means
algorithm.
Verification of confliction and unreachability in rule-based expert systems with model checking
Authors: Einollah pira, Mohammad Reza Zand Miralvand, Fakhteh Soltani
Date: 2014-04-10 00:00:00

It is important to find optimal solutions for structural errors in rule-based
expert systems .Solutions to discovering such errors by using model checking
techniques have already been proposed, but these solutions have problems such
as state space explosion. In this paper, to overcome these problems, we model
the rule-based systems as finite state transition systems and express
confliction and unreachability as Computation Tree Logic (CTL) logic formula
and then use the technique of model checking to detect confliction and
unreachability in rule-based systems with the model checker UPPAAL.
Avoiding Undesired Choices Using Intelligent Adaptive Systems
Authors: Amir Konigsberg
Date: 2014-04-10 00:00:00

We propose a number of heuristics that can be used for identifying when
intransitive choice behaviour is likely to occur in choice situations. We also
suggest two methods for avoiding undesired choice behaviour, namely transparent
communication and adaptive choice-set generation. We believe that these two
ways can contribute to the avoidance of decision biases in choice situations
that may often be regretted.
Computational Limits for Matrix Completion
Authors: Moritz Hardt, Raghu Meka, Prasad Raghavendra, Benjamin Weitz
Date: 2014-02-10 00:00:00
Tasks: Matrix Completion

Matrix Completion is the problem of recovering an unknown real-valued
low-rank matrix from a subsample of its entries. Important recent results show
that the problem can be solved efficiently under the assumption that the
unknown matrix is incoherent and the subsample is drawn uniformly at random.
Are these assumptions necessary?
  It is well known that Matrix Completion in its full generality is NP-hard.
However, little is known if make additional assumptions such as incoherence and
permit the algorithm to output a matrix of slightly higher rank. In this paper
we prove that Matrix Completion remains computationally intractable even if the
unknown matrix has rank $4$ but we are allowed to output any constant rank
matrix, and even if additionally we assume that the unknown matrix is
incoherent and are shown $90%$ of the entries. This result relies on the
conjectured hardness of the $4$-Coloring problem. We also consider the positive
semidefinite Matrix Completion problem. Here we show a similar hardness result
under the standard assumption that $\mathrm{P}\ne \mathrm{NP}.$
  Our results greatly narrow the gap between existing feasibility results and
computational lower bounds. In particular, we believe that our results give the
first complexity-theoretic justification for why distributional assumptions are
needed beyond the incoherence assumption in order to obtain positive results.
On the technical side, we contribute several new ideas on how to encode hard
combinatorial problems in low-rank optimization problems. We hope that these
techniques will be helpful in further understanding the computational limits of
Matrix Completion and related problems.
Open problem: Tightness of maximum likelihood semidefinite relaxations
Authors: Afonso S. Bandeira, Yuehaw Khoo, Amit Singer
Date: 2014-04-10 00:00:00

We have observed an interesting, yet unexplained, phenomenon: Semidefinite
programming (SDP) based relaxations of maximum likelihood estimators (MLE) tend
to be tight in recovery problems with noisy data, even when MLE cannot exactly
recover the ground truth. Several results establish tightness of SDP based
relaxations in the regime where exact recovery from MLE is possible. However,
to the best of our knowledge, their tightness is not understood beyond this
regime. As an illustrative example, we focus on the generalized Procrustes
problem.
Joint optimization of fitting & matching in multi-view reconstruction
Authors: Hossam Isack, Yuri Boykov
Date: 2013-03-11 00:00:00

Many standard approaches for geometric model fitting are based on pre-matched
image features. Typically, such pre-matching uses only feature appearances
(e.g. SIFT) and a large number of non-unique features must be discarded in
order to control the false positive rate. In contrast, we solve feature
matching and multi-model fitting problems in a joint optimization framework.
This paper proposes several fit-&-match energy formulations based on a
generalization of the assignment problem. We developed an efficient solver
based on min-cost-max-flow algorithm that finds near optimal solutions. Our
approach significantly increases the number of detected matches. In practice,
energy-based joint fitting & matching allows to increase the distance between
view-points previously restricted by robustness of local SIFT-matching and to
improve the model fitting accuracy when compared to state-of-the-art
multi-model fitting techniques.
Modeling Radiometric Uncertainty for Vision with Tone-mapped Color Images
Authors: Ayan Chakrabarti, Ying Xiong, Baochen Sun, Trevor Darrell, Daniel Scharstein, Todd Zickler, Kate Saenko
Date: 2013-11-27 00:00:00
Tasks: Tone Mapping

To produce images that are suitable for display, tone-mapping is widely used
in digital cameras to map linear color measurements into narrow gamuts with
limited dynamic range. This introduces non-linear distortion that must be
undone, through a radiometric calibration process, before computer vision
systems can analyze such photographs radiometrically. This paper considers the
inherent uncertainty of undoing the effects of tone-mapping. We observe that
this uncertainty varies substantially across color space, making some pixels
more reliable than others. We introduce a model for this uncertainty and a
method for fitting it to a given camera or imaging pipeline. Once fit, the
model provides for each pixel in a tone-mapped digital photograph a probability
distribution over linear scene colors that could have induced it. We
demonstrate how these distributions can be useful for visual inference by
incorporating them into estimation algorithms for a representative set of
vision tasks.
RANCOR: Non-Linear Image Registration with Total Variation Regularization
Authors: Martin Rajchl, John S. H. Baxter, Wu Qiu, Ali R. Khan, Aaron Fenster, Terry M. Peters, Jing Yuan
Date: 2014-04-09 00:00:00
Tasks: Brain Segmentation, Image Registration

Optimization techniques have been widely used in deformable registration,
allowing for the incorporation of similarity metrics with regularization
mechanisms. These regularization mechanisms are designed to mitigate the
effects of trivial solutions to ill-posed registration problems and to
otherwise ensure the resulting deformation fields are well-behaved. This paper
introduces a novel deformable registration algorithm, RANCOR, which uses
iterative convexification to address deformable registration problems under
total-variation regularization. Initial comparative results against four
state-of-the-art registration algorithms are presented using the Internet Brain
Segmentation Repository (IBSR) database.
Noisy Optimization: Convergence with a Fixed Number of Resamplings
Authors: Marie-Liesse Cauwet
Date: 2014-04-09 00:00:00

It is known that evolution strategies in continuous domains might not
converge in the presence of noise. It is also known that, under mild
assumptions, and using an increasing number of resamplings, one can mitigate
the effect of additive noise and recover convergence. We show new sufficient
conditions for the convergence of an evolutionary algorithm with constant
number of resamplings; in particular, we get fast rates (log-linear
convergence) provided that the variance decreases around the optimum slightly
faster than in the so-called multiplicative noise model. Keywords: Noisy
optimization, evolutionary algorithm, theory.
A Compact Linear Programming Relaxation for Binary Sub-modular MRF
Authors: Junyan Wang, Sai-Kit Yeung
Date: 2014-04-09 00:00:00
Tasks: Interactive Segmentation, Segmentation, Semantic Segmentation

We propose a novel compact linear programming (LP) relaxation for binary
sub-modular MRF in the context of object segmentation. Our model is obtained by
linearizing an $l_1^+$-norm derived from the quadratic programming (QP) form of
the MRF energy. The resultant LP model contains significantly fewer variables
and constraints compared to the conventional LP relaxation of the MRF energy.
In addition, unlike QP which can produce ambiguous labels, our model can be
viewed as a quasi-total-variation minimization problem, and it can therefore
preserve the discontinuities in the labels. We further establish a relaxation
bound between our LP model and the conventional LP model. In the experiments,
we demonstrate our method for the task of interactive object segmentation. Our
LP model outperforms QP when converting the continuous labels to binary labels
using different threshold values on the entire Oxford interactive segmentation
dataset. The computational complexity of our LP is of the same order as that of
the QP, and it is significantly lower than the conventional LP relaxation.