pwc-archive/papers-with-abstracts huggingface dataset:
Home Location Identification of Twitter Users
Authors: Jalal Mahmud, Jeffrey Nichols, Clemens Drews
Date: 2014-03-07 00:00:00

We present a new algorithm for inferring the home location of Twitter users
at different granularities, including city, state, time zone or geographic
region, using the content of users tweets and their tweeting behavior. Unlike
existing approaches, our algorithm uses an ensemble of statistical and
heuristic classifiers to predict locations and makes use of a geographic
gazetteer dictionary to identify place-name entities. We find that a
hierarchical classification approach, where time zone, state or geographic
region is predicted first and city is predicted next, can improve prediction
accuracy. We have also analyzed movement variations of Twitter users, built a
classifier to predict whether a user was travelling in a certain period of time
and use that to further improve the location detection accuracy. Experimental
evidence suggests that our algorithm works well in practice and outperforms the
best existing algorithms for predicting the home location of Twitter users.
Automated Tracking and Estimation for Control of Non-rigid Cloth
Authors: Marc D. Killpack
Date: 2014-03-07 00:00:00

This report is a summary of research conducted on cloth tracking for
automated textile manufacturing during a two semester long research course at
Georgia Tech. This work was completed in 2009. Advances in current sensing
technology such as the Microsoft Kinect would now allow me to relax certain
assumptions and generally improve the tracking performance. This is because a
major part of my approach described in this paper was to track features in a 2D
image and use these to estimate the cloth deformation. Innovations such as the
Kinect would improve estimation due to the automatic depth information obtained
when tracking 2D pixel locations. Additionally, higher resolution camera images
would probably give better quality feature tracking. However, although I would
use different technology now to implement this tracker, the algorithm described
and implemented in this paper is still a viable approach which is why I am
publishing this as a tech report for reference. In addition, although the
related work is a bit exhaustive, it will be useful to a reader who is new to
methods for tracking and estimation as well as modeling of cloth.
Fast Training of Convolutional Networks through FFTs
Authors: Michael Mathieu, Mikael Henaff, Yann Lecun
Date: 2013-12-20 00:00:00
Tasks: GPU

Convolutional networks are one of the most widely employed architectures in
computer vision and machine learning. In order to leverage their ability to
learn complex functions, large amounts of data are required for training.
Training a large convolutional network to produce state-of-the-art results can
take weeks, even when using modern GPUs. Producing labels using a trained
network can also be costly when dealing with web-scale datasets. In this work,
we present a simple algorithm which accelerates training and inference by a
significant factor, and can yield improvements of over an order of magnitude
compared to existing state-of-the-art implementations. This is done by
computing convolutions as pointwise products in the Fourier domain while
reusing the same transformed feature map many times. The algorithm is
implemented on a GPU architecture and addresses a number of related challenges.
Design a Persian Automated Plagiarism Detector (AMZPPD)
Authors: Maryam Mahmoodi, Mohammad Mahmoodi Varnamkhasti
Date: 2014-03-06 00:00:00

Currently there are lots of plagiarism detection approaches. But few of them
implemented and adapted for Persian languages. In this paper, our work on
designing and implementation of a plagiarism detection system based on
pre-processing and NLP technics will be described. And the results of testing
on a corpus will be presented.
Retrieval of Experiments with Sequential Dirichlet Process Mixtures in Model Space
Authors: Ritabrata Dutta, Sohan Seth, Samuel Kaski
Date: 2013-10-08 00:00:00
Tasks: Retrieval

We address the problem of retrieving relevant experiments given a query
experiment, motivated by the public databases of datasets in molecular biology
and other experimental sciences, and the need of scientists to relate to
earlier work on the level of actual measurement data. Since experiments are
inherently noisy and databases ever accumulating, we argue that a retrieval
engine should possess two particular characteristics. First, it should compare
models learnt from the experiments rather than the raw measurements themselves:
this allows incorporating experiment-specific prior knowledge to suppress noise
effects and focus on what is important. Second, it should be updated
sequentially from newly published experiments, without explicitly storing
either the measurements or the models, which is critical for saving storage
space and protecting data privacy: this promotes life long learning. We
formulate the retrieval as a ``supermodelling'' problem, of sequentially
learning a model of the set of posterior distributions, represented as sets of
MCMC samples, and suggest the use of Particle-Learning-based sequential
Dirichlet process mixture (DPM) for this purpose. The relevance measure for
retrieval is derived from the supermodel through the mixture representation. We
demonstrate the performance of the proposed retrieval method on simulated data
and molecular biological experiments.
Collaborative Filtering with Information-Rich and Information-Sparse Entities
Authors: Kai Zhu, Rui Wu, Lei Ying, R. Srikant
Date: 2014-03-06 00:00:00
Tasks: Clustering, Collaborative Filtering, Recommendation Systems

In this paper, we consider a popular model for collaborative filtering in
recommender systems where some users of a website rate some items, such as
movies, and the goal is to recover the ratings of some or all of the unrated
items of each user. In particular, we consider both the clustering model, where
only users (or items) are clustered, and the co-clustering model, where both
users and items are clustered, and further, we assume that some users rate many
items (information-rich users) and some users rate only a few items
(information-sparse users). When users (or items) are clustered, our algorithm
can recover the rating matrix with $\omega(MK \log M)$ noisy entries while $MK$
entries are necessary, where $K$ is the number of clusters and $M$ is the
number of items. In the case of co-clustering, we prove that $K^2$ entries are
necessary for recovering the rating matrix, and our algorithm achieves this
lower bound within a logarithmic factor when $K$ is sufficiently large. We
compare our algorithms with a well-known algorithms called alternating
minimization (AM), and a similarity score-based algorithm known as the
popularity-among-friends (PAF) algorithm by applying all three to the MovieLens
and Netflix data sets. Our co-clustering algorithm and AM have similar overall
error rates when recovering the rating matrix, both of which are lower than the
error rate under PAF. But more importantly, the error rate of our co-clustering
algorithm is significantly lower than AM and PAF in the scenarios of interest
in recommender systems: when recommending a few items to each user or when
recommending items to users who only rated a few items (these users are the
majority of the total user population). The performance difference increases
even more when noise is added to the datasets.
Discriminative Functional Connectivity Measures for Brain Decoding
Authors: Orhan Firat, Mete Ozay, Ilke Oztekin, Fatos T. Yarman Vural
Date: 2014-02-23 00:00:00
Tasks: Brain Decoding, Functional Connectivity, Retrieval, Time Series, Time Series Analysis

We propose a statistical learning model for classifying cognitive processes
based on distributed patterns of neural activation in the brain, acquired via
functional magnetic resonance imaging (fMRI). In the proposed learning method,
local meshes are formed around each voxel. The distance between voxels in the
mesh is determined by using a functional neighbourhood concept. In order to
define the functional neighbourhood, the similarities between the time series
recorded for voxels are measured and functional connectivity matrices are
constructed. Then, the local mesh for each voxel is formed by including the
functionally closest neighbouring voxels in the mesh. The relationship between
the voxels within a mesh is estimated by using a linear regression model. These
relationship vectors, called Functional Connectivity aware Local Relational
Features (FC-LRF) are then used to train a statistical learning machine. The
proposed method was tested on a recognition memory experiment, including data
pertaining to encoding and retrieval of words belonging to ten different
semantic categories. Two popular classifiers, namely k-nearest neighbour (k-nn)
and Support Vector Machine (SVM), are trained in order to predict the semantic
category of the item being retrieved, based on activation patterns during
encoding. The classification performance of the Functional Mesh Learning model,
which range in 62%-71% is superior to the classical multi-voxel pattern
analysis (MVPA) methods, which range in 40%-48%, for ten semantic categories.
Approximation Models of Combat in StarCraft 2
Authors: Ian Helmke, Daniel Kreymer, Karl Wiegand
Date: 2014-03-06 00:00:00
Tasks: Starcraft

Real-time strategy (RTS) games make heavy use of artificial intelligence
(AI), especially in the design of computerized opponents. Because of the
computational complexity involved in managing all aspects of these games, many
AI opponents are designed to optimize only a few areas of playing style. In
games like StarCraft 2, a very popular and recently released RTS, most AI
strategies revolve around economic and building efficiency: AI opponents try to
gather and spend all resources as quickly and effectively as possible while
ensuring that no units are idle. The aim of this work was to help address the
need for AI combat strategies that are not computationally intensive. Our goal
was to produce a computationally efficient model that is accurate at predicting
the results of complex battles between diverse armies, including which army
will win and how many units will remain. Our results suggest it may be possible
to develop a relatively simple approximation model of combat that can
accurately predict many battles that do not involve micromanagement. Future
designs of AI opponents may be able to incorporate such an approximation model
into their decision and planning systems to provide a challenge that is
strategically balanced across all aspects of play.
Backdoors to Tractable Answer-Set Programming
Authors: Johannes Klaus Fichte, Stefan Szeider
Date: 2011-04-14 00:00:00

Answer Set Programming (ASP) is an increasingly popular framework for
declarative programming that admits the description of problems by means of
rules and constraints that form a disjunctive logic program. In particular,
many AI problems such as reasoning in a nonmonotonic setting can be directly
formulated in ASP. Although the main problems of ASP are of high computational
complexity, located at the second level of the Polynomial Hierarchy, several
restrictions of ASP have been identified in the literature, under which ASP
problems become tractable.
  In this paper we use the concept of backdoors to identify new restrictions
that make ASP problems tractable. Small backdoors are sets of atoms that
represent "clever reasoning shortcuts" through the search space and represent a
hidden structure in the problem input. The concept of backdoors is widely used
in the areas of propositional satisfiability and constraint satisfaction. We
show that it can be fruitfully adapted to ASP. We demonstrate how backdoors can
serve as a unifying framework that accommodates several tractable restrictions
of ASP known from the literature. Furthermore, we show how backdoors allow us
to deploy recent algorithmic results from parameterized complexity theory to
the domain of answer set programming.
Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction
Authors: Manuel Lopes, Luis Montesano
Date: 2014-03-06 00:00:00
Tasks: Active Learning

In this survey we present different approaches that allow an intelligent
agent to explore autonomous its environment to gather information and learn
multiple tasks. Different communities proposed different solutions, that are in
many cases, similar and/or complementary. These solutions include active
learning, exploration/exploitation, online-learning and social learning. The
common aspect of all these approaches is that it is the agent to selects and
decides what information to gather next. Applications for these approaches
already include tutoring systems, autonomous grasping learning, navigation and
mapping and human-robot interaction. We discuss how these approaches are
related, explaining their similarities and their differences in terms of
problem assumptions and metrics of success. We consider that such an integrated
discussion will improve inter-disciplinary research and applications.
Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization
Authors: Cédric Févotte, Nicolas Dobigeon
Date: 2014-01-22 00:00:00
Tasks: Hyperspectral Unmixing

This paper introduces a robust mixing model to describe hyperspectral data
resulting from the mixture of several pure spectral signatures. This new model
not only generalizes the commonly used linear mixing model, but also allows for
possible nonlinear effects to be easily handled, relying on mild assumptions
regarding these nonlinearities. The standard nonnegativity and sum-to-one
constraints inherent to spectral unmixing are coupled with a group-sparse
constraint imposed on the nonlinearity component. This results in a new form of
robust nonnegative matrix factorization. The data fidelity term is expressed as
a beta-divergence, a continuous family of dissimilarity measures that takes the
squared Euclidean distance and the generalized Kullback-Leibler divergence as
special cases. The penalized objective is minimized with a block-coordinate
descent that involves majorization-minimization updates. Simulation results
obtained on synthetic and real data show that the proposed strategy competes
with state-of-the-art linear and nonlinear unmixing methods.
New Perspectives on k-Support and Cluster Norms
Authors: Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos
Date: 2014-03-06 00:00:00
Tasks: Matrix Completion

The $k$-support norm is a regularizer which has been successfully applied to
sparse vector prediction problems. We show that it belongs to a general class
of norms which can be formulated as a parameterized infimum over quadratics. We
further extend the $k$-support norm to matrices, and we observe that it is a
special case of the matrix cluster norm. Using this formulation we derive an
efficient algorithm to compute the proximity operator of both norms. This
improves upon the standard algorithm for the $k$-support norm and allows us to
apply proximal gradient methods to the cluster norm. We also describe how to
solve regularization problems which employ centered versions of these norms.
Finally, we apply the matrix regularizers to different matrix completion and
multitask learning datasets. Our results indicate that the spectral $k$-support
norm and the cluster norm give state of the art performance on these problems,
significantly outperforming trace norm and elastic net penalties.
Real-Time Classification of Twitter Trends
Authors: Arkaitz Zubiaga, Damiano Spina, Raquel Martínez, Víctor Fresno
Date: 2014-03-06 00:00:00
Tasks: Classification, General Classification, Marketing

Social media users give rise to social trends as they share about common
interests, which can be triggered by different reasons. In this work, we
explore the types of triggers that spark trends on Twitter, introducing a
typology with following four types: 'news', 'ongoing events', 'memes', and
'commemoratives'. While previous research has analyzed trending topics in a
long term, we look at the earliest tweets that produce a trend, with the aim of
categorizing trends early on. This would allow to provide a filtered subset of
trends to end users. We analyze and experiment with a set of straightforward
language-independent features based on the social spread of trends to
categorize them into the introduced typology. Our method provides an efficient
way to accurately categorize trending topics without need of external data,
enabling news organizations to discover breaking news in real-time, or to
quickly identify viral memes that might enrich marketing decisions, among
others. The analysis of social features also reveals patterns associated with
each type of trend, such as tweets about ongoing events being shorter as many
were likely sent from mobile devices, or memes having more retweets originating
from a few trend-setters.
Illumination,Expression and Occlusion Invariant Pose-Adaptive Face Recognition System for Real-Time Applications
Authors: Shireesha Chintalapati, M. V. Raghunadh
Date: 2014-03-06 00:00:00
Tasks: Face Alignment, Face Recognition, General Classification, Pose Estimation

Face recognition in real-time scenarios is mainly affected by illumination, expression and pose variations and also by occlusion. This paper presents the framework for pose adaptive component-based face recognition system. The framework proposed deals with all the above mentioned issues. The steps involved in the presented framework are (i) facial landmark localisation, (ii) facial component extraction, (iii) pre-processing of facial image (iv) facial pose estimation (v) feature extraction using Local Binary Pattern Histograms of each component followed by (vi) fusion of pose adaptive classification of components. By employing pose adaptive classification, the recognition process is carried out on some part of database, based on estimated pose, instead of applying the recognition process on the whole database. Pre-processing techniques employed to overcome the problems due to illumination variation are also discussed in this paper. Component-based techniques provide better recognition rates when face images are occluded compared to the holistic methods. Our method is simple, feasible and provides better results when compared to other holistic methods.
Efficient Point-to-Subspace Query in $\ell^1$ with Application to Robust Object Instance Recognition
Authors: Ju Sun, Yuqian Zhang, John Wright
Date: 2012-08-02 00:00:00
Tasks: Object Recognition

Motivated by vision tasks such as robust face and object recognition, we
consider the following general problem: given a collection of low-dimensional
linear subspaces in a high-dimensional ambient (image) space, and a query point
(image), efficiently determine the nearest subspace to the query in $\ell^1$
distance. In contrast to the naive exhaustive search which entails large-scale
linear programs, we show that the computational burden can be cut down
significantly by a simple two-stage algorithm: (1) projecting the query and
data-base subspaces into lower-dimensional space by random Cauchy matrix, and
solving small-scale distance evaluations (linear programs) in the projection
space to locate candidate nearest; (2) with few candidates upon independent
repetition of (1), getting back to the high-dimensional space and performing
exhaustive search. To preserve the identity of the nearest subspace with
nontrivial probability, the projection dimension typically is low-order
polynomial of the subspace dimension multiplied by logarithm of number of the
subspaces (Theorem 2.1). The reduced dimensionality and hence complexity
renders the proposed algorithm particularly relevant to vision application such
as robust face and object instance recognition that we investigate empirically.
Collaborative Representation for Classification, Sparse or Non-sparse?
Authors: Yang Wu, Vansteenberge Jarich, Masayuki Mukunoki, Michihiko Minoh
Date: 2014-03-06 00:00:00
Tasks: Classification, Dictionary Learning, Face Recognition, General Classification, Sparse Representation-based Classification

Sparse representation based classification (SRC) has been proved to be a
simple, effective and robust solution to face recognition. As it gets popular,
doubts on the necessity of enforcing sparsity starts coming up, and primary
experimental results showed that simply changing the $l_1$-norm based
regularization to the computationally much more efficient $l_2$-norm based
non-sparse version would lead to a similar or even better performance. However,
that's not always the case. Given a new classification task, it's still unclear
which regularization strategy (i.e., making the coefficients sparse or
non-sparse) is a better choice without trying both for comparison. In this
paper, we present as far as we know the first study on solving this issue,
based on plenty of diverse classification experiments. We propose a scoring
function for pre-selecting the regularization strategy using only the dataset
size, the feature dimensionality and a discrimination score derived from a
given feature representation. Moreover, we show that when dictionary learning
is taking into account, non-sparse representation has a more significant
superiority to sparse representation. This work is expected to enrich our
understanding of sparse/non-sparse collaborative representation for
classification and motivate further research activities.
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction
Authors: Jian Zhou, Olga G. Troyanskaya
Date: 2014-03-06 00:00:00
Tasks: Prediction, Protein Secondary Structure Prediction, Protein Structure Prediction

Predicting protein secondary structure is a fundamental problem in protein
structure prediction. Here we present a new supervised generative stochastic
network (GSN) based method to predict local secondary structure with deep
hierarchical representations. GSN is a recently proposed deep learning
technique (Bengio & Thibodeau-Laufer, 2013) to globally train deep generative
model. We present the supervised extension of GSN, which learns a Markov chain
to sample from a conditional distribution, and applied it to protein structure
prediction. To scale the model to full-sized, high-dimensional data, like
protein sequences with hundreds of amino acids, we introduce a convolutional
architecture, which allows efficient learning across multiple layers of
hierarchical representations. Our architecture uniquely focuses on predicting
structured low-level labels informed with both low and high-level
representations learned by the model. In our application this corresponds to
labeling the secondary structure state of each amino-acid residue. We trained
and tested the model on separate sets of non-homologous proteins sharing less
than 30% sequence identity. Our model achieves 66.4% Q8 accuracy on the CB513
dataset, better than the previously reported best performance 64.9% (Wang et
al., 2011) for this challenging secondary structure prediction problem.
Minimax Optimal Bayesian Aggregation
Authors: Yun Yang, David B. Dunson
Date: 2014-03-06 00:00:00

It is generally believed that ensemble approaches, which combine multiple
algorithms or models, can outperform any single algorithm at machine learning
tasks, such as prediction. In this paper, we propose Bayesian convex and linear
aggregation approaches motivated by regression applications. We show that the
proposed approach is minimax optimal when the true data-generating model is a
convex or linear combination of models in the list. Moreover, the method can
adapt to sparsity structure in which certain models should receive zero
weights, and the method is tuning parameter free unlike competitors. More
generally, under an M-open view when the truth falls outside the space of all
convex/linear combinations, our theory suggests that the posterior measure
tends to concentrate on the best approximation of the truth at the minimax
rate. We illustrate the method through simulation studies and several
applications.