pwc-archive/papers-with-abstracts huggingface dataset:
Image patch analysis of sunspots and active regions. II. Clustering via matrix factorization
Authors: Kevin R. Moon, Veronique Delouille, Jimmy J. Li, Ruben De Visscher, Fraser Watson, Alfred O. Hero III
Date: 2015-04-10 00:00:00
Tasks: Clustering, General Classification

Separating active regions that are quiet from potentially eruptive ones is a
key issue in Space Weather applications. Traditional classification schemes
such as Mount Wilson and McIntosh have been effective in relating an active
region large scale magnetic configuration to its ability to produce eruptive
events. However, their qualitative nature prevents systematic studies of an
active region's evolution for example. We introduce a new clustering of active
regions that is based on the local geometry observed in Line of Sight
magnetogram and continuum images. We use a reduced-dimension representation of
an active region that is obtained by factoring the corresponding data matrix
comprised of local image patches. Two factorizations can be compared via the
definition of appropriate metrics on the resulting factors. The distances
obtained from these metrics are then used to cluster the active regions. We
find that these metrics result in natural clusterings of active regions. The
clusterings are related to large scale descriptors of an active region such as
its size, its local magnetic field distribution, and its complexity as measured
by the Mount Wilson classification scheme. We also find that including data
focused on the neutral line of an active region can result in an increased
correspondence between our clustering results and other active region
descriptors such as the Mount Wilson classifications and the $R$ value. We
provide some recommendations for which metrics, matrix factorization
techniques, and regions of interest to use to study active regions.
Inference in topic models: sparsity and trade-off
Authors: Khoat Than, Tu Bao Ho
Date: 2015-12-10 00:00:00
Tasks: Topic Models

Topic models are popular for modeling discrete data (e.g., texts, images,
videos, links), and provide an efficient way to discover hidden
structures/semantics in massive data. One of the core problems in this field is
the posterior inference for individual data instances. This problem is
particularly important in streaming environments, but is often intractable. In
this paper, we investigate the use of the Frank-Wolfe algorithm (FW) for
recovering sparse solutions to posterior inference. From detailed elucidation
of both theoretical and practical aspects, FW exhibits many interesting
properties which are beneficial to topic modeling. We then employ FW to design
fast methods, including ML-FW, for learning latent Dirichlet allocation (LDA)
at large scales. Extensive experiments show that to reach the same
predictiveness level, ML-FW can perform tens to thousand times faster than
existing state-of-the-art methods for learning LDA from massive/streaming data.
THCHS-30 : A Free Chinese Speech Corpus
Authors: Dong Wang, Xuewei Zhang
Date: 2015-12-07 00:00:00
Tasks: speech-recognition, Speech Recognition

Speech data is crucially important for speech recognition research. There are
quite some speech databases that can be purchased at prices that are reasonable
for most research institutes. However, for young people who just start research
activities or those who just gain initial interest in this direction, the cost
for data is still an annoying barrier. We support the `free data' movement in
speech recognition: research institutes (particularly supported by public
funds) publish their data freely so that new researchers can obtain sufficient
data to kick of their career. In this paper, we follow this trend and release a
free Chinese speech database THCHS-30 that can be used to build a full- edged
Chinese speech recognition system. We report the baseline system established
with this database, including the performance under highly noisy conditions.
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Authors: Tianshui Chen, Liang Lin, Lingbo Liu, Xiaonan Luo, Xuelong. Li
Date: 2015-11-13 00:00:00
Tasks: object-detection, Object Detection, Representation Learning, RGB Salient Object Detection, Salient Object Detection

Salient object detection increasingly receives attention as an important
component or step in several pattern recognition and image processing tasks.
Although a variety of powerful saliency models have been intensively proposed,
they usually involve heavy feature (or model) engineering based on priors (or
assumptions) about the properties of objects and backgrounds. Inspired by the
effectiveness of recently developed feature learning, we provide a novel Deep
Image Saliency Computing (DISC) framework for fine-grained image saliency
computing. In particular, we model the image saliency from both the coarse- and
fine-level observations, and utilize the deep convolutional neural network
(CNN) to learn the saliency representation in a progressive manner.
Specifically, our saliency model is built upon two stacked CNNs. The first CNN
generates a coarse-level saliency map by taking the overall image as the input,
roughly identifying saliency regions in the global context. Furthermore, we
integrate superpixel-based local context information in the first CNN to refine
the coarse-level saliency map. Guided by the coarse saliency map, the second
CNN focuses on the local context to produce fine-grained and accurate saliency
map while preserving object details. For a testing image, the two CNNs
collaboratively conduct the saliency computing in one shot. Our DISC framework
is capable of uniformly highlighting the objects-of-interest from complex
background while preserving well object details. Extensive experiments on
several standard benchmarks suggest that DISC outperforms other
state-of-the-art methods and it also generalizes well across datasets without
additional training. The executable version of DISC is available online:
http://vision.sysu.edu.cn/projects/DISC.
A Population Background for Nonparametric Density-Based Clustering
Authors: José E. Chacón
Date: 2014-08-06 00:00:00
Tasks: Clustering

Despite its popularity, it is widely recognized that the investigation of
some theoretical aspects of clustering has been relatively sparse. One of the
main reasons for this lack of theoretical results is surely the fact that,
whereas for other statistical problems the theoretical population goal is
clearly defined (as in regression or classification), for some of the
clustering methodologies it is difficult to specify the population goal to
which the data-based clustering algorithms should try to get close. This paper
aims to provide some insight into the theoretical foundations of clustering by
focusing on two main objectives: to provide an explicit formulation for the
ideal population goal of the modal clustering methodology, which understands
clusters as regions of high density; and to present two new loss functions,
applicable in fact to any clustering methodology, to evaluate the performance
of a data-based clustering algorithm with respect to the ideal population goal.
In particular, it is shown that only mild conditions on a sequence of density
estimators are needed to ensure that the sequence of modal clusterings that
they induce is consistent.
On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods
Authors: Anne-Marie Lyne, Mark Girolami, Yves Atchadé, Heiko Strathmann, Daniel Simpson
Date: 2013-06-17 00:00:00
Tasks: Bayesian Inference

A large number of statistical models are "doubly-intractable": the likelihood
normalising term, which is a function of the model parameters, is intractable,
as well as the marginal likelihood (model evidence). This means that standard
inference techniques to sample from the posterior, such as Markov chain Monte
Carlo (MCMC), cannot be used. Examples include, but are not confined to,
massive Gaussian Markov random fields, autologistic models and Exponential
random graph models. A number of approximate schemes based on MCMC techniques,
Approximate Bayesian computation (ABC) or analytic approximations to the
posterior have been suggested, and these are reviewed here. Exact MCMC schemes,
which can be applied to a subset of doubly-intractable distributions, have also
been developed and are described in this paper. As yet, no general method
exists which can be applied to all classes of models with doubly-intractable
posteriors. In addition, taking inspiration from the Physics literature, we
study an alternative method based on representing the intractable likelihood as
an infinite series. Unbiased estimates of the likelihood can then be obtained
by finite time stochastic truncation of the series via Russian Roulette
sampling, although the estimates are not necessarily positive. Results from the
Quantum Chromodynamics literature are exploited to allow the use of possibly
negative estimates in a pseudo-marginal MCMC scheme such that expectations with
respect to the posterior distribution are preserved. The methodology is
reviewed on well-known examples such as the parameters in Ising models, the
posterior for Fisher-Bingham distributions on the $d$-Sphere and a large-scale
Gaussian Markov Random Field model describing the Ozone Column data. This leads
to a critical assessment of the strengths and weaknesses of the methodology
with pointers to ongoing research.
Gated networks: an inventory
Authors: Olivier Sigaud, Clément Masson, David Filliat, Freek Stulp
Date: 2015-12-10 00:00:00
Tasks: Activity Recognition

Gated networks are networks that contain gating connections, in which the
outputs of at least two neurons are multiplied. Initially, gated networks were
used to learn relationships between two input sources, such as pixels from two
images. More recently, they have been applied to learning activity recognition
or multi-modal representations. The aims of this paper are threefold: 1) to
explain the basic computations in gated networks to the non-expert, while
adopting a standpoint that insists on their symmetric nature. 2) to serve as a
quick reference guide to the recent literature, by providing an inventory of
applications of these networks, as well as recent extensions to the basic
architecture. 3) to suggest future research directions and applications.
3D Reconstruction of Crime Scenes and Design Considerations for an Interactive Investigation Tool
Authors: Erkan Bostanci
Date: 2015-12-10 00:00:00
Tasks: 3D Reconstruction

Crime Scene Investigation (CSI) is a carefully planned systematic process
with the purpose of acquiring physical evidences to shed light upon the
physical reality of the crime and eventually detect the identity of the
criminal. Capturing images and videos of the crime scene is an important part
of this process in order to conduct a deeper analysis on the digital evidence
for possible hints. This work brings this idea further to use the acquired
footage for generating a 3D model of the crime scene. Results show that
realistic reconstructions can be obtained using sophisticated computer vision
techniques. The paper also discusses a number of important design
considerations describing key features that should be present in a powerful
interactive CSI analysis tool.
Enhanced image feature coverage: Key-point selection using genetic algorithms
Authors: Erkan Bostanci
Date: 2015-12-10 00:00:00

Coverage of image features play an important role in many vision algorithms
since their distribution affect the estimated homography. This paper presents a
Genetic Algorithm (GA) in order to select the optimal set of features yielding
maximum coverage of the image which is measured by a robust method based on
spatial statistics. It is shown with statistical tests on two datasets that the
metric yields better coverage and this is also confirmed by an accuracy test on
the computed homography for the original set and the newly selected set of
features. Results have demonstrated that the new set has similar performance in
terms of the accuracy of the computed homography with the original one with an
extra benefit of using fewer number of features ultimately reducing the time
required for descriptor calculation and matching.
Evaluation of Object Detection Proposals Under Condition Variations
Authors: Fahimeh Rezazadegan, Sareh Shirazi, Michael Milford, Ben Upcroft
Date: 2015-12-10 00:00:00
Tasks: Object, object-detection, Object Detection

Object detection is a fundamental task in many computer vision applications,
therefore the importance of evaluating the quality of object detection is well
acknowledged in this domain. This process gives insight into the capabilities
of methods in handling environmental changes. In this paper, a new method for
object detection is introduced that combines the Selective Search and
EdgeBoxes. We tested these three methods under environmental variations. Our
experiments demonstrate the outperformance of the combination method under
illumination and view point variations.
Predicting proximity with ambient mobile sensors for non-invasive health diagnostics
Authors: Sylvester Olubolu Orimaye, Foo Chuan Leong, Chen Hui Lee, Eddy Cheng Han Ng
Date: 2015-12-10 00:00:00
Tasks: Diagnostic, Specificity

Modern smart phones are becoming helpful in the areas of Internet-Of-Things
(IoT) and ambient health intelligence. By learning data from several mobile
sensors, we detect nearness of the human body to a mobile device in a
three-dimensional space with no physical contact with the device for
non-invasive health diagnostics. We show that the human body generates wave
patterns that interact with other naturally occurring ambient signals that
could be measured by mobile sensors, such as, temperature, humidity, magnetic
field, acceleration, gravity, and light. This interaction consequentially
alters the patterns of the naturally occurring signals, and thus, exhibits
characteristics that could be learned to predict the nearness of the human body
to a mobile device, hence provide diagnostic information for medical
practitioners. Our prediction technique achieved 88.75% accuracy and 88.3%
specificity.
Approximate Message Passing with Restricted Boltzmann Machine Priors
Authors: Eric W. Tramel, Angélique Drémeau, Florent Krzakala
Date: 2015-02-23 00:00:00
Tasks: compressed sensing

Approximate Message Passing (AMP) has been shown to be an excellent
statistical approach to signal inference and compressed sensing problem. The
AMP framework provides modularity in the choice of signal prior; here we
propose a hierarchical form of the Gauss-Bernouilli prior which utilizes a
Restricted Boltzmann Machine (RBM) trained on the signal support to push
reconstruction performance beyond that of simple iid priors for signals whose
support can be well represented by a trained binary RBM. We present and analyze
two methods of RBM factorization and demonstrate how these affect signal
reconstruction performance within our proposed algorithm. Finally, using the
MNIST handwritten digit dataset, we show experimentally that using an RBM
allows AMP to approach oracle-support performance.
Learning Linguistic Biomarkers for Predicting Mild Cognitive Impairment using Compound Skip-grams
Authors: Sylvester Olubolu Orimaye, Kah Yee Tai, Jojo Sze-Meng Wong, Chee Piau Wong
Date: 2015-11-08 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic

Predicting Mild Cognitive Impairment (MCI) is currently a challenge as
existing diagnostic criteria rely on neuropsychological examinations. Automated
Machine Learning (ML) models that are trained on verbal utterances of MCI
patients can aid diagnosis. Using a combination of skip-gram features, our
model learned several linguistic biomarkers to distinguish between 19 patients
with MCI and 19 healthy control individuals from the DementiaBank language
transcript clinical dataset. Results show that a model with compound of
skip-grams has better AUC and could help ML prediction on small MCI data
sample.
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey
Authors: Li Wang, Dennis Sng
Date: 2015-12-10 00:00:00
Tasks: Deep Learning, Face Recognition, General Classification, image-classification, Image Classification, Object, object-detection, Object Detection, Object Tracking, Scene Labeling, speech-recognition, Speech Recognition

Deep learning has recently achieved very promising results in a wide range of
areas such as computer vision, speech recognition and natural language
processing. It aims to learn hierarchical representations of data by using deep
architecture models. In a smart city, a lot of data (e.g. videos captured from
many distributed sensors) need to be automatically processed and analyzed. In
this paper, we review the deep learning algorithms applied to video analytics
of smart city in terms of different research topics: object detection, object
tracking, face recognition, image classification and scene labeling.
How the Voynich Manuscript was created
Authors: Torsten Timm
Date: 2014-07-24 00:00:00
Tasks: Relation, Text Generation

The Voynich manuscript is a medieval book written in an unknown script. This
paper studies the relation between similarly spelled words in the Voynich
manuscript. By means of a detailed analysis of similar spelled words it was
possible to reveal the text generation method used for the Voynich manuscript.
Scaling POMDPs For Selecting Sellers in E-markets-Extended Version
Authors: Athirai A. Irissappane, Frans A. Oliehoek, Jie Zhang
Date: 2015-11-30 00:00:00

In multiagent e-marketplaces, buying agents need to select good sellers by
querying other buyers (called advisors). Partially Observable Markov Decision
Processes (POMDPs) have shown to be an effective framework for optimally
selecting sellers by selectively querying advisors. However, current solution
methods do not scale to hundreds or even tens of agents operating in the
e-market. In this paper, we propose the Mixture of POMDP Experts (MOPE)
technique, which exploits the inherent structure of trust-based domains, such
as the seller selection problem in e-markets, by aggregating the solutions of
smaller sub-POMDPs. We propose a number of variants of the MOPE approach that
we analyze theoretically and empirically. Experiments show that MOPE can scale
up to a hundred agents thereby leveraging the presence of more advisors to
significantly improve buyer satisfaction.
Partial Reinitialisation for Optimisers
Authors: Ilia Zintchenko, Matthew Hastings, Nathan Wiebe, Ethan Brown, Matthias Troyer
Date: 2015-12-09 00:00:00

Heuristic optimisers which search for an optimal configuration of variables
relative to an objective function often get stuck in local optima where the
algorithm is unable to find further improvement. The standard approach to
circumvent this problem involves periodically restarting the algorithm from
random initial configurations when no further improvement can be found. We
propose a method of partial reinitialization, whereby, in an attempt to find a
better solution, only sub-sets of variables are re-initialised rather than the
whole configuration. Much of the information gained from previous runs is hence
retained. This leads to significant improvements in the quality of the solution
found in a given time for a variety of optimisation problems in machine
learning.
Learning measures of semi-additive behaviour
Authors: Hamidreza Chinaei, Mohsen Rais-Ghasem, Frank Rudzicz
Date: 2015-12-09 00:00:00

In business analytics, measure values, such as sales numbers or volumes of
cargo transported, are often summed along values of one or more corresponding
categories, such as time or shipping container. However, not every measure
should be added by default (e.g., one might more typically want a mean over the
heights of a set of people); similarly, some measures should only be summed
within certain constraints (e.g., population measures need not be summed over
years). In systems such as Watson Analytics, the exact additive behaviour of a
measure is often determined by a human expert. In this work, we propose a small
set of features for this issue. We use these features in a case-based reasoning
approach, where the system suggests an aggregation behaviour, with 86% accuracy
in our collected dataset.
Minimally Supervised Feature Selection for Classification (Master's Thesis, University Politehnica of Bucharest)
Authors: Alexandra Maria Radu
Date: 2015-12-09 00:00:00
Tasks: feature selection, General Classification, Transfer Learning

In the context of the highly increasing number of features that are available
nowadays we design a robust and fast method for feature selection. The method
tries to select the most representative features that are independent from each
other, but are strong together. We propose an algorithm that requires very
limited labeled data (as few as one labeled frame per class) and can
accommodate as many unlabeled samples. We also present here the supervised
approach from which we started. We compare our two formulations with
established methods like AdaBoost, SVM, Lasso, Elastic Net and FoBa and show
that our method is much faster and it has constant training time. Moreover, the
unsupervised approach outperforms all the methods with which we compared and
the difference might be quite prominent. The supervised approach is in most
cases better than the other methods, especially when the number of training
shots is very limited. All that the algorithm needs is to choose from a pool of
positively correlated features. The methods are evaluated on the
Youtube-Objects dataset of videos and on MNIST digits dataset, while at
training time we also used features obtained on CIFAR10 dataset and others
pre-trained on ImageNet dataset. Thereby, we also proved that transfer learning
is useful, even though the datasets differ very much: from low-resolution
centered images from 10 classes, to high-resolution images with objects from
1000 classes occurring in different regions of the images or to very difficult
videos with very high intraclass variance. 7
ShapeNet: An Information-Rich 3D Model Repository
Authors: Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qi-Xing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, Fisher Yu
Date: 2015-12-09 00:00:00
Tasks: Data Visualization, model

We present ShapeNet: a richly-annotated, large-scale repository of shapes
represented by 3D CAD models of objects. ShapeNet contains 3D models from a
multitude of semantic categories and organizes them under the WordNet taxonomy.
It is a collection of datasets providing many semantic annotations for each 3D
model such as consistent rigid alignments, parts and bilateral symmetry planes,
physical sizes, keywords, as well as other planned annotations. Annotations are
made available through a public web-based interface to enable data
visualization of object attributes, promote data-driven geometric analysis, and
provide a large-scale quantitative benchmark for research in computer graphics
and vision. At the time of this technical report, ShapeNet has indexed more
than 3,000,000 models, 220,000 models out of which are classified into 3,135
categories (WordNet synsets). In this report we describe the ShapeNet effort as
a whole, provide details for all currently available datasets, and summarize
future plans.