pwc-archive/papers-with-abstracts huggingface dataset:
Doubly Decomposing Nonparametric Tensor Regression
Authors: Masaaki Imaizumi, Kohei Hayashi
Date: 2015-06-19 00:00:00
Tasks: regression, Tensor Decomposition

Nonparametric extension of tensor regression is proposed. Nonlinearity in a
high-dimensional tensor space is broken into simple local functions by
incorporating low-rank tensor decomposition. Compared to naive nonparametric
approaches, our formulation considerably improves the convergence rate of
estimation while maintaining consistency with the same function class under
specific conditions. To estimate local functions, we develop a Bayesian
estimator with the Gaussian process prior. Experimental results show its
theoretical properties and high performance in terms of predicting a summary
statistic of a real complex network.
A hybrid approach based segmentation technique for brain tumor in MRI Images
Authors: D. Anithadevi, K. Perumal
Date: 2016-03-08 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Specificity

Automatic image segmentation becomes very crucial for tumor detection in
medical image processing.In general, manual and semi automatic segmentation
techniques require more time and knowledge. However these drawbacks had
overcome by automatic segmentation still there needs to develop more
appropriate techniques for medical image segmentation. Therefore, we proposed
hybrid approach based image segmentation using the combined features of region
growing and threshold based segmentation techniques. It is followed by
pre-processing stage to provide an accurate brain tumor extraction by the help
of Magnetic Resonance Imaging (MRI). If the tumor has holes, the region growing
segmentation algorithm cannot reveal but the proposed hybrid segmentation
technique can be achieved and the result as well improved. Hence the result
used to made assessment with the various performance measures as DICE, JACCARD
similarity, accuracy, sensitivity and specificity. These similarity measures
have been extensively used for evaluation with the ground truth of each
processed image and its results are compared and analyzed.
Effective Mean-Field Inference Method for Nonnegative Boltzmann Machines
Authors: Muneki Yasuda
Date: 2016-03-08 00:00:00

Nonnegative Boltzmann machines (NNBMs) are recurrent probabilistic neural
network models that can describe multi-modal nonnegative data. NNBMs form
rectified Gaussian distributions that appear in biological neural network
models, positive matrix factorization, nonnegative matrix factorization, and so
on. In this paper, an effective inference method for NNBMs is proposed that
uses the mean-field method, referred to as the Thouless--Anderson--Palmer
equation, and the diagonal consistency method, which was recently proposed.
Stochastic dual averaging methods using variance reduction techniques for regularized empirical risk minimization problems
Authors: Tomoya Murata, Taiji Suzuki
Date: 2016-03-08 00:00:00
Tasks: BIG-bench Machine Learning

We consider a composite convex minimization problem associated with
regularized empirical risk minimization, which often arises in machine
learning. We propose two new stochastic gradient methods that are based on
stochastic dual averaging method with variance reduction. Our methods generate
a sparser solution than the existing methods because we do not need to take the
average of the history of the solutions. This is favorable in terms of both
interpretability and generalization. Moreover, our methods have theoretical
support for both a strongly and a non-strongly convex regularizer and achieve
the best known convergence rates among existing nonaccelerated stochastic
gradient methods.
Frequency estimation in three-phase power systems with harmonic contamination: A multistage quaternion Kalman filtering approach
Authors: Sayed Pouria Talebi, Danilo P. Mandic
Date: 2016-03-08 00:00:00

Motivated by the need for accurate frequency information, a novel algorithm
for estimating the fundamental frequency and its rate of change in three-phase
power systems is developed. This is achieved through two stages of Kalman
filtering. In the first stage a quaternion extended Kalman filter, which
provides a unified framework for joint modeling of voltage measurements from
all the phases, is used to estimate the instantaneous phase increment of the
three-phase voltages. The phase increment estimates are then used as
observations of the extended Kalman filter in the second stage that accounts
for the dynamic behavior of the system frequency and simultaneously estimates
the fundamental frequency and its rate of change. The framework is then
extended to account for the presence of harmonics. Finally, the concept is
validated through simulation on both synthetic and real-world data.
Formal Ontology Learning on Factual IS-A Corpus in English using Description Logics
Authors: Sourish Dasgupta, Ankur Padia, Kushal Shah, Prasenjit Majumder
Date: 2013-12-25 00:00:00

Ontology Learning (OL) is the computational task of generating a knowledge
base in the form of an ontology given an unstructured corpus whose content is
in natural language (NL). Several works can be found in this area most of which
are limited to statistical and lexico-syntactic pattern matching based
techniques Light-Weight OL. These techniques do not lead to very accurate
learning mostly because of several linguistic nuances in NL. Formal OL is an
alternative (less explored) methodology were deep linguistics analysis is made
using theory and tools found in computational linguistics to generate formal
axioms and definitions instead simply inducing a taxonomy. In this paper we
propose "Description Logic (DL)" based formal OL framework for learning factual
IS-A type sentences in English. We claim that semantic construction of IS-A
sentences is non trivial. Hence, we also claim that such sentences requires
special studies in the context of OL before any truly formal OL can be
proposed. We introduce a learner tool, called DLOL_IS-A, that generated such
ontologies in the owl format. We have adopted "Gold Standard" based OL
evaluation on IS-A rich WCL v.1.1 dataset and our own Community representative
IS-A dataset. We observed significant improvement of DLOL_IS-A when compared to
the light-weight OL tool Text2Onto and formal OL tool FRED.
A Scalable and Extensible Framework for Superposition-Structured Models
Authors: Shenjian Zhao, Cong Xie, Zhihua Zhang
Date: 2015-09-08 00:00:00

In many learning tasks, structural models usually lead to better
interpretability and higher generalization performance. In recent years,
however, the simple structural models such as lasso are frequently proved to be
insufficient. Accordingly, there has been a lot of work on
"superposition-structured" models where multiple structural constraints are
imposed. To efficiently solve these "superposition-structured" statistical
models, we develop a framework based on a proximal Newton-type method.
Employing the smoothed conic dual approach with the LBFGS updating formula, we
propose a scalable and extensible proximal quasi-Newton (SEP-QN) framework.
Empirical analysis on various datasets shows that our framework is potentially
powerful, and achieves super-linear convergence rate for optimizing some
popular "superposition-structured" statistical models such as the fused sparse
group lasso.
Negatively Correlated Search
Authors: Ke Tang, Peng Yang, Xin Yao
Date: 2015-04-20 00:00:00
Tasks: Diversity, Evolutionary Algorithms

Evolutionary Algorithms (EAs) have been shown to be powerful tools for
complex optimization problems, which are ubiquitous in both communication and
big data analytics. This paper presents a new EA, namely Negatively Correlated
Search (NCS), which maintains multiple individual search processes in parallel
and models the search behaviors of individual search processes as probability
distributions. NCS explicitly promotes negatively correlated search behaviors
by encouraging differences among the probability distributions (search
behaviors). By this means, individual search processes share information and
cooperate with each other to search diverse regions of a search space, which
makes NCS a promising method for non-convex optimization. The cooperation
scheme of NCS could also be regarded as a novel diversity preservation scheme
that, different from other existing schemes, directly promotes diversity at the
level of search behaviors rather than merely trying to maintain diversity among
candidate solutions. Empirical studies showed that NCS is competitive to
well-established search methods in the sense that NCS achieved the best overall
performance on 20 multimodal (non-convex) continuous optimization problems. The
advantages of NCS over state-of-the-art approaches are also demonstrated with a
case study on the synthesis of unequally spaced linear antenna arrays.
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
Authors: Bryan Hooi, Neil Shah, Alex Beutel, Stephan Gunnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos
Date: 2015-11-19 00:00:00
Tasks: Bayesian Inference, Fraud Detection

Review fraud is a pervasive problem in online commerce, in which fraudulent
sellers write or purchase fake reviews to manipulate perception of their
products and services. Fake reviews are often detected based on several signs,
including 1) they occur in short bursts of time; 2) fraudulent user accounts
have skewed rating distributions. However, these may both be true in any given
dataset. Hence, in this paper, we propose an approach for detecting fraudulent
reviews which combines these 2 approaches in a principled manner, allowing
successful detection even when one of these signs is not present. To combine
these 2 approaches, we formulate our Bayesian Inference for Rating Data (BIRD)
model, a flexible Bayesian model of user rating behavior. Based on our model we
formulate a likelihood-based suspiciousness metric, Normalized Expected
Surprise Total (NEST). We propose a linear-time algorithm for performing
Bayesian inference using our model and computing the metric. Experiments on
real data show that BIRDNEST successfully spots review fraud in large,
real-world graphs: the 50 most suspicious users of the Flipkart platform
flagged by our algorithm were investigated and all identified as fraudulent by
domain experts at Flipkart.
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements
Authors: Kuldeep Kulkarni, Suhas Lohit, Pavan Turaga, Ronan Kerviche, Amit Ashok
Date: 2016-01-26 00:00:00
Proceeding: CVPR 2016
Tasks: Real-Time Visual Tracking, Visual Tracking

The goal of this paper is to present a non-iterative and more importantly an
extremely fast algorithm to reconstruct images from compressively sensed (CS)
random measurements. To this end, we propose a novel convolutional neural
network (CNN) architecture which takes in CS measurements of an image as input
and outputs an intermediate reconstruction. We call this network, ReconNet. The
intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the
final reconstructed image. On a standard dataset of images we show significant
improvements in reconstruction results (both in terms of PSNR and time
complexity) over state-of-the-art iterative CS reconstruction algorithms at
various measurement rates. Further, through qualitative experiments on real
data collected using our block single pixel camera (SPC), we show that our
network is highly robust to sensor noise and can recover visually better
quality images than competitive algorithms at extremely low sensing rates of
0.1 and 0.04. To demonstrate that our algorithm can recover semantically
informative images even at a low measurement rate of 0.01, we present a very
robust proof of concept real-time visual tracking application.
$k$-center Clustering under Perturbation Resilience
Authors: Maria-Florina Balcan, Nika Haghtalab, Colin White
Date: 2015-05-14 00:00:00
Tasks: Clustering

The $k$-center problem is a canonical and long-studied facility location and
clustering problem with many applications in both its symmetric and asymmetric
forms. Both versions of the problem have tight approximation factors on worst
case instances. Therefore to improve on these ratios, one must go beyond the
worst case.
  In this work, we take this approach and provide strong positive results both
for the asymmetric and symmetric $k$-center problems under a natural input
stability (promise) condition called $\alpha$-perturbation resilience [Bilu and
Linia 2012], which states that the optimal solution does not change under any
alpha-factor perturbation to the input distances. We provide algorithms that
give strong guarantees simultaneously for stable and non-stable instances: our
algorithms always inherit the worst-case guarantees of clustering approximation
algorithms, and output the optimal solution if the input is $2$-perturbation
resilient. Furthermore, we prove our result is tight by showing symmetric
$k$-center under $(2-\epsilon)$-perturbation resilience is hard unless $NP=RP$.
  The impact of our results are multifaceted. This is the first tight result
for any problem under perturbation resilience. Furthermore, our results
illustrate a surprising relationship between symmetric and asymmetric
$k$-center instances under perturbation resilience. Unlike approximation ratio,
for which symmetric $k$-center is easily solved to a factor of 2 but asymmetric
$k$-center cannot be approximated to any constant factor, both symmetric and
asymmetric $k$-center can be solved optimally under resilience to
2-perturbations. Finally, our guarantees in the setting where only part of the
data satisfies perturbation resilience makes these algorithms more applicable
to real-life instances.
Blur Robust Optical Flow using Motion Channel
Authors: Wenbin Li, Yang Chen, JeeHang Lee, Gang Ren, Darren Cosker
Date: 2016-03-07 00:00:00
Tasks: Optical Flow Estimation

It is hard to estimate optical flow given a realworld video sequence with
camera shake and other motion blur. In this paper, we first investigate the
blur parameterization for video footage using near linear motion elements. we
then combine a commercial 3D pose sensor with an RGB camera, in order to film
video footage of interest together with the camera motion. We illustrates that
this additional camera motion/trajectory channel can be embedded into a hybrid
framework by interleaving an iterative blind deconvolution and warping based
optical flow scheme. Our method yields improved accuracy within three other
state-of-the-art baselines given our proposed ground truth blurry sequences;
and several other realworld sequences filmed by our imaging system.
Drift Robust Non-rigid Optical Flow Enhancement for Long Sequences
Authors: Wenbin Li, Darren Cosker, Matthew Brown
Date: 2016-03-07 00:00:00
Tasks: Optical Flow Estimation

It is hard to densely track a nonrigid object in long term, which is a
fundamental research issue in the computer vision community. This task often
relies on estimating pairwise correspondences between images over time where
the error is accumulated and leads to a drift issue. In this paper, we
introduce a novel optimization framework with an Anchor Patch constraint. It is
supposed to significantly reduce overall errors given long sequences containing
non-rigidly deformable objects. Our framework can be applied to any dense
tracking algorithm, e.g. optical flow. We demonstrate the success of our
approach by showing significant error reduction on 6 popular optical flow
algorithms applied to a range of real-world nonrigid benchmarks. We also
provide quantitative analysis of our approach given synthetic occlusions and
image noise.
Online Sparse Linear Regression
Authors: Dean Foster, Satyen Kale, Howard Karloff
Date: 2016-03-07 00:00:00
Tasks: regression

We consider the online sparse linear regression problem, which is the problem
of sequentially making predictions observing only a limited number of features
in each round, to minimize regret with respect to the best sparse linear
regressor, where prediction accuracy is measured by square loss. We give an
inefficient algorithm that obtains regret bounded by $\tilde{O}(\sqrt{T})$
after $T$ prediction rounds. We complement this result by showing that no
algorithm running in polynomial time per iteration can achieve regret bounded
by $O(T^{1-\delta})$ for any constant $\delta > 0$ unless $\text{NP} \subseteq
\text{BPP}$. This computational hardness result resolves an open problem
presented in COLT 2014 (Kale, 2014) and also posed by Zolghadr et al. (2013).
This hardness result holds even if the algorithm is allowed to access more
features than the best sparse linear regressor up to a logarithmic factor in
the dimension.
Guided macro-mutation in a graded energy based genetic algorithm for protein structure prediction
Authors: Mahmood A. Rashid, Sumaiya Iqbal, Firas Khatib, Md. Tamjidul Hoque, Abdul Sattar
Date: 2016-03-07 00:00:00
Tasks: Protein Structure Prediction

Protein structure prediction is considered as one of the most challenging and
computationally intractable combinatorial problem. Thus, the efficient modeling
of convoluted search space, the clever use of energy functions, and more
importantly, the use of effective sampling algorithms become crucial to address
this problem. For protein structure modeling, an off-lattice model provides
limited scopes to exercise and evaluate the algorithmic developments due to its
astronomically large set of data-points. In contrast, an on-lattice model
widens the scopes and permits studying the relatively larger proteins because
of its finite set of data-points. In this work, we took the full advantage of
an on-lattice model by using a face-centered-cube lattice that has the highest
packing density with the maximum degree of freedom. We proposed a graded
energy-strategically mixes the Miyazawa-Jernigan (MJ) energy with the
hydrophobic-polar (HP) energy-based genetic algorithm (GA) for conformational
search. In our application, we introduced a 2x2 HP energy guided macro-mutation
operator within the GA to explore the best possible local changes exhaustively.
Conversely, the 20x20 MJ energy model-the ultimate objective function of our GA
that needs to be minimized-considers the impacts amongst the 20 different amino
acids and allow searching the globally acceptable conformations. On a set of
benchmark proteins, our proposed approach outperformed state-of-the-art
approaches in terms of the free energy levels and the root-mean-square
deviations.
Hierarchical Models as Marginals of Hierarchical Models
Authors: Guido Montufar, Johannes Rauh
Date: 2015-08-14 00:00:00

We investigate the representation of hierarchical models in terms of
marginals of other hierarchical models with smaller interactions. We focus on
binary variables and marginals of pairwise interaction models whose hidden
variables are conditionally independent given the visible variables. In this
case the problem is equivalent to the representation of linear subspaces of
polynomials by feedforward neural networks with soft-plus computational units.
We show that every hidden variable can freely model multiple interactions among
the visible variables, which allows us to generalize and improve previous
results. In particular, we show that a restricted Boltzmann machine with less
than $[ 2(\log(v)+1) / (v+1) ] 2^v-1$ hidden binary variables can approximate
every distribution of $v$ visible binary variables arbitrarily well, compared
to $2^{v-1}-1$ from the best previously known result.
Authenticating users through their arm movement patterns
Authors: Rajesh Kumar, Vir V. Phoha, Rahul Raina
Date: 2016-03-07 00:00:00

In this paper, we propose four continuous authentication designs by using the
characteristics of arm movements while individuals walk. The first design uses
acceleration of arms captured by a smartwatch's accelerometer sensor, the
second design uses the rotation of arms captured by a smartwatch's gyroscope
sensor, third uses the fusion of both acceleration and rotation at the
feature-level and fourth uses the fusion at score-level. Each of these designs
is implemented by using four classifiers, namely, k nearest neighbors (k-NN)
with Euclidean distance, Logistic Regression, Multilayer Perceptrons, and
Random Forest resulting in a total of sixteen authentication mechanisms. These
authentication mechanisms are tested under three different environments, namely
an intra-session, inter-session on a dataset of 40 users and an inter-phase on
a dataset of 12 users. The sessions of data collection were separated by at
least ten minutes, whereas the phases of data collection were separated by at
least three months. Under the intra-session environment, all of the twelve
authentication mechanisms achieve a mean dynamic false accept rate (DFAR) of 0%
and dynamic false reject rate (DFRR) of 0%. For the inter-session environment,
feature level fusion-based design with classifier k-NN achieves the best error
rates that are a mean DFAR of 2.2% and DFRR of 4.2%. The DFAR and DFRR
increased from 5.68% and 4.23% to 15.03% and 14.62% respectively when feature
level fusion-based design with classifier k-NN was tested under the inter-phase
environment on a dataset of 12 users.
Elastic Functional Coding of Riemannian Trajectories
Authors: Rushil Anirudh, Pavan Turaga, Jingyong Su, Anuj Srivastava
Date: 2016-03-07 00:00:00
Tasks: Action Analysis, Retrieval

Visual observations of dynamic phenomena, such as human actions, are often
represented as sequences of smoothly-varying features . In cases where the
feature spaces can be structured as Riemannian manifolds, the corresponding
representations become trajectories on manifolds. Analysis of these
trajectories is challenging due to non-linearity of underlying spaces and
high-dimensionality of trajectories. In vision problems, given the nature of
physical systems involved, these phenomena are better characterized on a
low-dimensional manifold compared to the space of Riemannian trajectories. For
instance, if one does not impose physical constraints of the human body, in
data involving human action analysis, the resulting representation space will
have highly redundant features. Learning an effective, low-dimensional
embedding for action representations will have a huge impact in the areas of
search and retrieval, visualization, learning, and recognition. The difficulty
lies in inherent non-linearity of the domain and temporal variability of
actions that can distort any traditional metric between trajectories. To
overcome these issues, we use the framework based on transported square-root
velocity fields (TSRVF); this framework has several desirable properties,
including a rate-invariant metric and vector space representations. We propose
to learn an embedding such that each action trajectory is mapped to a single
point in a low-dimensional Euclidean space, and the trajectories that differ
only in temporal rates map to the same point. We utilize the TSRVF
representation, and accompanying statistical summaries of Riemannian
trajectories, to extend existing coding methods such as PCA, KSVD and Label
Consistent KSVD to Riemannian trajectories or more generally to Riemannian
functions.