pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive-Rate Sparse Signal Reconstruction With Application in Compressive Background Subtraction
Authors: Joao F. C. Mota, Nikos Deligiannis, Aswin C. Sankaranarayanan, Volkan Cevher, Miguel R. D. Rodrigues
Date: 2015-03-11 00:00:00
Tasks: Video Background Subtraction

We propose and analyze an online algorithm for reconstructing a sequence of
signals from a limited number of linear measurements. The signals are assumed
sparse, with unknown support, and evolve over time according to a generic
nonlinear dynamical model. Our algorithm, based on recent theoretical results
for $\ell_1$-$\ell_1$ minimization, is recursive and computes the number of
measurements to be taken at each time on-the-fly. As an example, we apply the
algorithm to compressive video background subtraction, a problem that can be
stated as follows: given a set of measurements of a sequence of images with a
static background, simultaneously reconstruct each image while separating its
foreground from the background. The performance of our method is illustrated on
sequences of real images: we observe that it allows a dramatic reduction in the
number of measurements with respect to state-of-the-art compressive background
subtraction schemes.
A Multi-Gene Genetic Programming Application for Predicting Students Failure at School
Authors: J. O. Orove, N. E. Osegi, B. O. Eke
Date: 2015-03-11 00:00:00

Several efforts to predict student failure rate (SFR) at school accurately
still remains a core problem area faced by many in the educational sector. The
procedure for forecasting SFR are rigid and most often times require data
scaling or conversion into binary form such as is the case of the logistic
model which may lead to lose of information and effect size attenuation. Also,
the high number of factors, incomplete and unbalanced dataset, and black boxing
issues as in Artificial Neural Networks and Fuzzy logic systems exposes the
need for more efficient tools. Currently the application of Genetic Programming
(GP) holds great promises and has produced tremendous positive results in
different sectors. In this regard, this study developed GPSFARPS, a software
application to provide a robust solution to the prediction of SFR using an
evolutionary algorithm known as multi-gene genetic programming. The approach is
validated by feeding a testing data set to the evolved GP models. Result
obtained from GPSFARPS simulations show its unique ability to evolve a suitable
failure rate expression with a fast convergence at 30 generations from a
maximum specified generation of 500. The multi-gene system was also able to
minimize the evolved model expression and accurately predict student failure
rate using a subset of the original expression
Polarization Measurement of High Dimensional Social Media Messages With Support Vector Machine Algorithm Using Mapreduce
Authors: Ferhat Özgür Çatak
Date: 2014-10-10 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis

In this article, we propose a new Support Vector Machine (SVM) training
algorithm based on distributed MapReduce technique. In literature, there are a
lots of research that shows us SVM has highest generalization property among
classification algorithms used in machine learning area. Also, SVM classifier
model is not affected by correlations of the features. But SVM uses quadratic
optimization techniques in its training phase. The SVM algorithm is formulated
as quadratic optimization problem. Quadratic optimization problem has $O(m^3)$
time and $O(m^2)$ space complexity, where m is the training set size. The
computation time of SVM training is quadratic in the number of training
instances. In this reason, SVM is not a suitable classification algorithm for
large scale dataset classification. To solve this training problem we developed
a new distributed MapReduce method developed. Accordingly, (i) SVM algorithm is
trained in distributed dataset individually; (ii) then merge all support
vectors of classifier model in every trained node; and (iii) iterate these two
steps until the classifier model converges to the optimal classifier function.
In the implementation phase, large scale social media dataset is presented in
TFxIDF matrix. The matrix is used for sentiment analysis to get polarization
value. Two and three class models are created for classification method.
Confusion matrices of each classification model are presented in tables. Social
media messages corpus consists of 108 public and 66 private universities
messages in Turkey. Twitter is used for source of corpus. Twitter user messages
are collected using Twitter Streaming API. Results are shown in graphics and
tables.
Benchmarking NLopt and state-of-art algorithms for Continuous Global Optimization via Hybrid IACO$_\mathbb{R}$
Authors: Udit Kumar, Sumit Soman, Jayadeva
Date: 2015-03-11 00:00:00
Tasks: Benchmarking, global-optimization

This paper presents a comparative analysis of the performance of the
Incremental Ant Colony algorithm for continuous optimization
($IACO_\mathbb{R}$), with different algorithms provided in the NLopt library.
The key objective is to understand how the various algorithms in the NLopt
library perform in combination with the Multi Trajectory Local Search (Mtsls1)
technique. A hybrid approach has been introduced in the local search strategy
by the use of a parameter which allows for probabilistic selection between
Mtsls1 and a NLopt algorithm. In case of stagnation, the algorithm switch is
made based on the algorithm being used in the previous iteration. The paper
presents an exhaustive comparison on the performance of these approaches on
Soft Computing (SOCO) and Congress on Evolutionary Computation (CEC) 2014
benchmarks. For both benchmarks, we conclude that the best performing algorithm
is a hybrid variant of Mtsls1 with BFGS for local search.
Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder
Authors: Xi Zhang, Yanwei Fu, Andi Zang, Leonid Sigal, Gady Agam
Date: 2015-03-11 00:00:00
Tasks: General Classification

We propose a method for using synthetic data to help learning classifiers.
Synthetic data, even is generated based on real data, normally results in a
shift from the distribution of real data in feature space. To bridge the gap
between the real and synthetic data, and jointly learn from synthetic and real
data, this paper proposes a Multichannel Autoencoder(MCAE). We show that by
suing MCAE, it is possible to learn a better feature representation for
classification. To evaluate the proposed approach, we conduct experiments on
two types of datasets. Experimental results on two datasets validate the
efficiency of our MCAE model and our methodology of generating synthetic data.
A Neurodynamical System for finding a Minimal VC Dimension Classifier
Authors: Jayadeva, Sumit Soman, Amit Bhaya
Date: 2015-03-11 00:00:00

The recently proposed Minimal Complexity Machine (MCM) finds a hyperplane
classifier by minimizing an exact bound on the Vapnik-Chervonenkis (VC)
dimension. The VC dimension measures the capacity of a learning machine, and a
smaller VC dimension leads to improved generalization. On many benchmark
datasets, the MCM generalizes better than SVMs and uses far fewer support
vectors than the number used by SVMs. In this paper, we describe a neural
network based on a linear dynamical system, that converges to the MCM solution.
The proposed MCM dynamical system is conducive to an analogue circuit
implementation on a chip or simulation using Ordinary Differential Equation
(ODE) solvers. Numerical experiments on benchmark datasets from the UCI
repository show that the proposed approach is scalable and accurate, as we
obtain improved accuracies and fewer number of support vectors (upto 74.3%
reduction) with the MCM dynamical system.
L_1-regularized Boltzmann machine learning using majorizer minimization
Authors: Masayuki Ohzeki
Date: 2015-03-11 00:00:00
Tasks: BIG-bench Machine Learning, compressed sensing

We propose an inference method to estimate sparse interactions and biases
according to Boltzmann machine learning. The basis of this method is $L_1$
regularization, which is often used in compressed sensing, a technique for
reconstructing sparse input signals from undersampled outputs. $L_1$
regularization impedes the simple application of the gradient method, which
optimizes the cost function that leads to accurate estimations, owing to the
cost function's lack of smoothness. In this study, we utilize the majorizer
minimization method, which is a well-known technique implemented in
optimization problems, to avoid the non-smoothness of the cost function. By
using the majorizer minimization method, we elucidate essentially relevant
biases and interactions from given data with seemingly strongly-correlated
components.
Single Image Super Resolution via Manifold Approximation
Authors: Chinh Dang, Hayder Radha
Date: 2014-10-13 00:00:00
Tasks: Clustering, Image Super-Resolution, Super-Resolution

Image super-resolution remains an important research topic to overcome the
limitations of physical acquisition systems, and to support the development of
high resolution displays. Previous example-based super-resolution approaches
mainly focus on analyzing the co-occurrence properties of low resolution and
high-resolution patches. Recently, we proposed a novel single image
super-resolution approach based on linear manifold approximation of the
high-resolution image-patch space [1]. The image super-resolution problem is
then formulated as an optimization problem of searching for the best matched
high resolution patch in the manifold for a given low-resolution patch. We
developed a novel technique based on the l1 norm sparse graph to learn a set of
low dimensional affine spaces or tangent subspaces of the high-resolution patch
manifold. The optimization problem is then solved based on the learned set of
tangent subspaces. In this paper, we build on our recent work as follows.
First, we consider and analyze each tangent subspace as one point in a
Grassmann manifold, which helps to compute geodesic pairwise distances among
these tangent subspaces. Second, we develop a min-max algorithm to select an
optimal subset of tangent subspaces. This optimal subset reduces the
computational cost while still preserving the quality of the reconstructed
high-resolution image. Third, and to further achieve lower computational
complexity, we perform hierarchical clustering on the optimal subset based on
Grassmann manifold distances. Finally, we analytically prove the validity of
the proposed Grassmann-distance based clustering. A comparison of the obtained
results with other state-of-the-art methods clearly indicates the viability of
the new proposed framework.
Technical Analysis on Financial Forecasting
Authors: S. Gopal Krishna Patro, Pragyan Parimita Sahoo, Ipsita Panda, Kishore Kumar Sahu
Date: 2015-03-10 00:00:00

Financial forecasting is an estimation of future financial outcomes for a
company, industry, country using historical internal accounting and sales data.
We may predict the future outcome of BSE_SENSEX practically by some soft
computing techniques and can also optimized using PSO (Particle Swarm
Optimization), EA (Evolutionary Algorithm) or DEA (Differential Evolutionary
Algorithm) etc. PSO is a biologically inspired computational search &
optimization method developed in 1995 by Dr. Eberhart and Dr. Kennedy based on
the social behaviors of fish schooling or birds flocking. PSO is a promising
method to train Artificial Neural Network (ANN). It is easy to implement then
Genetic Algorithm except few parameters are adjusted. PSO is a random & pattern
search technique based on populating of particle. In PSO, the particles are
having some position and velocity in the search space. Two terms are used in
PSO one is Local Best and another one is Global Best. To optimize problems that
are like Irregular, Noisy, Change over time, Static etc. PSO uses a classic
optimization method such as Gradient Decent & Quasi-Newton Methods. The
observation and review of few related studies in the last few years, focusing
on function of PSO, modification of PSO and operation that have implemented
using PSO like function optimization, ANN Training & Fuzzy Control etc.
Differential Evolution is an efficient EA technique for optimization of
numerical problems, financial problems etc. PSO technique is introduced due to
the swarming behavior of animals which is the collective behavior of similar
size that aggregates together.
Quantum Structure in Cognition, Origins, Developments, Successes and Expectations
Authors: Diederik Aerts, Sandro Sozzo
Date: 2015-03-10 00:00:00

We provide an overview of the results we have attained in the last decade on
the identification of quantum structures in cognition and, more specifically,
in the formalization and representation of natural concepts. We firstly discuss
the quantum foundational reasons that led us to investigate the mechanisms of
formation and combination of concepts in human reasoning, starting from the
empirically observed deviations from classical logical and probabilistic
structures. We then develop our quantum-theoretic perspective in Fock space
which allows successful modeling of various sets of cognitive experiments
collected by different scientists, including ourselves. In addition, we
formulate a unified explanatory hypothesis for the presence of quantum
structures in cognitive processes, and discuss our recent discovery of further
quantum aspects in concept combinations, namely, 'entanglement' and
'indistinguishability'. We finally illustrate perspectives for future research.
A Case Based Reasoning Approach for Answer Reranking in Question Answering
Authors: Karl-Heinz Weis
Date: 2015-03-10 00:00:00
Tasks: Graph Similarity, Question Answering, Reranking

In this document I present an approach to answer validation and reranking for
question answering (QA) systems. A cased-based reasoning (CBR) system judges
answer candidates for questions from annotated answer candidates for earlier
questions. The promise of this approach is that user feedback will result in
improved answers of the QA system, due to the growing case base. In the paper,
I present the adequate structuring of the case base and the appropriate
selection of relevant similarity measures, in order to solve the answer
validation problem. The structural case base is built from annotated MultiNet
graphs, which provide representations for natural language expressions, and
corresponding graph similarity measures. I cover a priori relations to
experienced answer candidates for former questions. I compare the CBR System
results to current approaches in an experiment integrating CBR into an existing
framework for answer validation and reranking. This integration is achieved by
adding CBR-related features to the input of a learned ranking model that
determines the final answer ranking. In the experiments based on QA@CLEF
questions, the best learned models make heavy use of CBR features. Observing
the results with a continually growing case base, I present a positive effect
of the size of the case base on the accuracy of the CBR subsystem.
Robust recovery of complex exponential signals from random Gaussian projections via low rank Hankel matrix reconstruction
Authors: Jian-Feng Cai, Xiaobo Qu, Weiyu Xu, Gui-Bo Ye
Date: 2015-03-10 00:00:00
Tasks: compressed sensing

This paper explores robust recovery of a superposition of $R$ distinct
complex exponential functions from a few random Gaussian projections. We assume
that the signal of interest is of $2N-1$ dimensional and $R<<2N-1$. This
framework covers a large class of signals arising from real applications in
biology, automation, imaging science, etc. To reconstruct such a signal, our
algorithm is to seek a low-rank Hankel matrix of the signal by minimizing its
nuclear norm subject to the consistency on the sampled data. Our theoretical
results show that a robust recovery is possible as long as the number of
projections exceeds $O(R\ln^2N)$. No incoherence or separation condition is
required in our proof. Our method can be applied to spectral compressed sensing
where the signal of interest is a superposition of $R$ complex sinusoids.
Compared to existing results, our result here does not need any separation
condition on the frequencies, while achieving better or comparable bounds on
the number of measurements. Furthermore, our method provides theoretical
guidance on how many samples are required in the state-of-the-art non-uniform
sampling in NMR spectroscopy. The performance of our algorithm is further
demonstrated by numerical experiments.
Multi-dimensional sparse structured signal approximation using split Bregman iterations
Authors: Yoann Isaac, Quentin Barthélemy, Jamal Atif, Cédric Gouy-Pailler, Michèle Sebag
Date: 2013-03-21 00:00:00

The paper focuses on the sparse approximation of signals using overcomplete
representations, such that it preserves the (prior) structure of
multi-dimensional signals. The underlying optimization problem is tackled using
a multi-dimensional split Bregman optimization approach. An extensive empirical
evaluation shows how the proposed approach compares to the state of the art
depending on the signal features.
Single stream parallelization of generalized LSTM-like RNNs on a GPU
Authors: Kyuyeon Hwang, Wonyong Sung
Date: 2015-03-10 00:00:00
Tasks: GPU

Recurrent neural networks (RNNs) have shown outstanding performance on
processing sequence data. However, they suffer from long training time, which
demands parallel implementations of the training procedure. Parallelization of
the training algorithms for RNNs are very challenging because internal
recurrent paths form dependencies between two different time frames. In this
paper, we first propose a generalized graph-based RNN structure that covers the
most popular long short-term memory (LSTM) network. Then, we present a
parallelization approach that automatically explores parallelisms of arbitrary
RNNs by analyzing the graph structure. The experimental results show that the
proposed approach shows great speed-up even with a single training stream, and
further accelerates the training when combined with multiple parallel training
streams.
Parallel Statistical Multi-resolution Estimation
Authors: Jan Lebert, Lutz Künneke, Johannes Hagemann, Stephan C. Kramer
Date: 2015-03-10 00:00:00
Tasks: CPU, Super-Resolution

We discuss several strategies to implement Dykstra's projection algorithm on
NVIDIA's compute unified device architecture (CUDA). Dykstra's algorithm is the
central step in and the computationally most expensive part of statistical
multi-resolution methods. It projects a given vector onto the intersection of
convex sets. Compared with a CPU implementation our CUDA implementation is one
order of magnitude faster. For a further speed up and to reduce memory
consumption we have developed a new variant, which we call incomplete Dykstra's
algorithm. Implemented in CUDA it is one order of magnitude faster than the
CUDA implementation of the standard Dykstra algorithm. As sample application we
discuss using the incomplete Dykstra's algorithm as preprocessor for the
recently developed super-resolution optical fluctuation imaging (SOFI) method
(Dertinger et al. 2009). We show that statistical multi-resolution estimation
can enhance the resolution improvement of the plain SOFI algorithm just as the
Fourier-reweighting of SOFI. The results are compared in terms of their power
spectrum and their Fourier ring correlation (Saxton and Baumeister 1982). The
Fourier ring correlation indicates that the resolution for typical second order
SOFI images can be improved by about 30 per cent. Our results show that a
careful parallelization of Dykstra's algorithm enables its use in large-scale
statistical multi-resolution analyses.
Doubly Robust Policy Evaluation and Optimization
Authors: Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li
Date: 2015-03-10 00:00:00
Tasks: Decision Making, Multi-Armed Bandits, Sequential Decision Making

We study sequential decision making in environments where rewards are only
partially observed, but can be modeled as a function of observed contexts and
the chosen action by the decision maker. This setting, known as contextual
bandits, encompasses a wide variety of applications such as health care,
content recommendation and Internet advertising. A central task is evaluation
of a new policy given historic data consisting of contexts, actions and
received rewards. The key challenge is that the past data typically does not
faithfully represent proportions of actions taken by a new policy. Previous
approaches rely either on models of rewards or models of the past policy. The
former are plagued by a large bias whereas the latter have a large variance. In
this work, we leverage the strengths and overcome the weaknesses of the two
approaches by applying the doubly robust estimation technique to the problems
of policy evaluation and optimization. We prove that this approach yields
accurate value estimates when we have either a good (but not necessarily
consistent) model of rewards or a good (but not necessarily consistent) model
of past policy. Extensive empirical comparison demonstrates that the doubly
robust estimation uniformly improves over existing techniques, achieving both
lower variance in value estimation and better policies. As such, we expect the
doubly robust approach to become common practice in policy evaluation and
optimization.
Minimax Optimal Rates of Estimation in High Dimensional Additive Models: Universal Phase Transition
Authors: Ming Yuan, Ding-Xuan Zhou
Date: 2015-03-10 00:00:00
Tasks: Additive models, regression

We establish minimax optimal rates of convergence for estimation in a high
dimensional additive model assuming that it is approximately sparse. Our
results reveal an interesting phase transition behavior universal to this class
of high dimensional problems. In the {\it sparse regime} when the components
are sufficiently smooth or the dimensionality is sufficiently large, the
optimal rates are identical to those for high dimensional linear regression,
and therefore there is no additional cost to entertain a nonparametric model.
Otherwise, in the so-called {\it smooth regime}, the rates coincide with the
optimal rates for estimating a univariate function, and therefore they are
immune to the "curse of dimensionality".
Short Text Hashing Improved by Integrating Multi-Granularity Topics and Tags
Authors: Jiaming Xu, Bo Xu, Guanhua Tian, Jun Zhao, Fangyuan Wang, Hong-Wei Hao
Date: 2015-03-10 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Due to computational and storage efficiencies of compact binary codes,
hashing has been widely used for large-scale similarity search. Unfortunately,
many existing hashing methods based on observed keyword features are not
effective for short texts due to the sparseness and shortness. Recently, some
researchers try to utilize latent topics of certain granularity to preserve
semantic similarity in hash codes beyond keyword matching. However, topics of
certain granularity are not adequate to represent the intrinsic semantic
information. In this paper, we present a novel unified approach for short text
Hashing using Multi-granularity Topics and Tags, dubbed HMTT. In particular, we
propose a selection method to choose the optimal multi-granularity topics
depending on the type of dataset, and design two distinct hashing strategies to
incorporate multi-granularity topics. We also propose a simple and effective
method to exploit tags to enhance the similarity of related texts. We carry out
extensive experiments on one short text dataset as well as on one normal text
dataset. The results demonstrate that our approach is effective and
significantly outperforms baselines on several evaluation metrics.