pwc-archive/papers-with-abstracts huggingface dataset:
Spectral Unmixing of Hyperspectral Imagery using Multilayer NMF
Authors: Roozbeh Rajabi, Hassan Ghassemian
Date: 2014-08-12 00:00:00
Tasks: Hyperspectral Unmixing

Hyperspectral images contain mixed pixels due to low spatial resolution of
hyperspectral sensors. Spectral unmixing problem refers to decomposing mixed
pixels into a set of endmembers and abundance fractions. Due to nonnegativity
constraint on abundance fractions, nonnegative matrix factorization (NMF)
methods have been widely used for solving spectral unmixing problem. In this
letter we proposed using multilayer NMF (MLNMF) for the purpose of
hyperspectral unmixing. In this approach, spectral signature matrix can be
modeled as a product of sparse matrices. In fact MLNMF decomposes the
observation matrix iteratively in a number of layers. In each layer, we applied
sparseness constraint on spectral signature matrix as well as on abundance
fractions matrix. In this way signatures matrix can be sparsely decomposed
despite the fact that it is not generally a sparse matrix. The proposed
algorithm is applied on synthetic and real datasets. Synthetic data is
generated based on endmembers from USGS spectral library. AVIRIS Cuprite
dataset has been used as a real dataset for evaluation of proposed method.
Results of experiments are quantified based on SAD and AAD measures. Results in
comparison with previously proposed methods show that the multilayer approach
can unmix data more effectively.
Online Learning via Sequential Complexities
Authors: Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
Date: 2010-06-06 00:00:00
Tasks: Learning Theory, Prediction

We consider the problem of sequential prediction and provide tools to study
the minimax value of the associated game. Classical statistical learning theory
provides several useful complexity measures to study learning with i.i.d. data.
Our proposed sequential complexities can be seen as extensions of these
measures to the sequential setting. The developed theory is shown to yield
precise learning guarantees for the problem of sequential prediction. In
particular, we show necessary and sufficient conditions for online learnability
in the setting of supervised learning. Several examples show the utility of our
framework: we can establish learnability without having to exhibit an explicit
online learning algorithm.
Generalization and Robustness of Batched Weighted Average Algorithm with V-geometrically Ergodic Markov Data
Authors: Nguyen Viet Cuong, Lam Si Tung Ho, Vu Dinh
Date: 2014-06-12 00:00:00
Tasks: General Classification, regression

We analyze the generalization and robustness of the batched weighted average
algorithm for V-geometrically ergodic Markov data. This algorithm is a good
alternative to the empirical risk minimization algorithm when the latter
suffers from overfitting or when optimizing the empirical risk is hard. For the
generalization of the algorithm, we prove a PAC-style bound on the training
sample size for the expected $L_1$-loss to converge to the optimal loss when
training data are V-geometrically ergodic Markov chains. For the robustness, we
show that if the training target variable's values contain bounded noise, then
the generalization bound of the algorithm deviates at most by the range of the
noise. Our results can be applied to the regression problem, the classification
problem, and the case where there exists an unknown deterministic target
hypothesis.
Internal and external dynamics in language: Evidence from verb regularity in a historical corpus of English
Authors: Christine F. Cuskley, Martina Pugliese, Claudio Castellano, Francesca Colaiori, Vittorio Loreto, Francesca Tria
Date: 2014-08-12 00:00:00

Human languages are rule governed, but almost invariably these rules have
exceptions in the form of irregularities. Since rules in language are efficient
and productive, the persistence of irregularity is an anomaly. How does
irregularity linger in the face of internal (endogenous) and external
(exogenous) pressures to conform to a rule? Here we address this problem by
taking a detailed look at simple past tense verbs in the Corpus of Historical
American English. The data show that the language is open, with many new verbs
entering. At the same time, existing verbs might tend to regularize or
irregularize as a consequence of internal dynamics, but overall, the amount of
irregularity sustained by the language stays roughly constant over time.
Despite continuous vocabulary growth, and presumably, an attendant increase in
expressive power, there is no corresponding growth in irregularity. We analyze
the set of irregulars, showing they may adhere to a set of minority rules,
allowing for increased stability of irregularity over time. These findings
contribute to the debate on how language systems become rule governed, and how
and why they sustain exceptions to rules, providing insight into the interplay
between the emergence and maintenance of rules and exceptions in language.
Multiple Instance Learning with Bag Dissimilarities
Authors: Veronika Cheplygina, David M. J. Tax, Marco Loog
Date: 2013-09-22 00:00:00
Tasks: Multiple Instance Learning

Multiple instance learning (MIL) is concerned with learning from sets (bags)
of objects (instances), where the individual instance labels are ambiguous. In
this setting, supervised learning cannot be applied directly. Often,
specialized MIL methods learn by making additional assumptions about the
relationship of the bag labels and instance labels. Such assumptions may fit a
particular dataset, but do not generalize to the whole range of MIL problems.
Other MIL methods shift the focus of assumptions from the labels to the overall
(dis)similarity of bags, and therefore learn from bags directly. We propose to
represent each bag by a vector of its dissimilarities to other bags in the
training set, and treat these dissimilarities as a feature representation. We
show several alternatives to define a dissimilarity between bags and discuss
which definitions are more suitable for particular MIL problems. The
experimental results show that the proposed approach is computationally
inexpensive, yet very competitive with state-of-the-art algorithms on a wide
range of MIL datasets.
Mapping the stereotyped behaviour of freely-moving fruit flies
Authors: Gordon J. Berman, Daniel M. Choi, William Bialek, Joshua W. Shaevitz
Date: 2013-10-16 00:00:00
Tasks: Diversity

Most animals possess the ability to actuate a vast diversity of movements,
ostensibly constrained only by morphology and physics. In practice, however, a
frequent assumption in behavioral science is that most of an animal's
activities can be described in terms of a small set of stereotyped motifs. Here
we introduce a method for mapping the behavioral space of organisms, relying
only upon the underlying structure of postural movement data to organize and
classify behaviors. We find that six different drosophilid species each perform
a mix of non-stereotyped actions and over one hundred hierarchically-organized,
stereotyped behaviors. Moreover, we use this approach to compare these species'
behavioral spaces, systematically identifying subtle behavioral differences
between closely-related species.
Comparing Nonparametric Bayesian Tree Priors for Clonal Reconstruction of Tumors
Authors: Amit G. Deshwar, Shankar Vembu, Quaid Morris
Date: 2014-08-11 00:00:00

Statistical machine learning methods, especially nonparametric Bayesian
methods, have become increasingly popular to infer clonal population structure
of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant
process (CRP), a popular construction used in nonparametric mixture models, to
infer the phylogeny and genotype of major subclonal lineages represented in the
population of cancer cells. We also propose new split-merge updates tailored to
the subclonal reconstruction problem that improve the mixing time of Markov
chains. In comparisons with the tree-structured stick breaking prior used in
PhyloSub, we demonstrate superior mixing and running time using the treeCRP
with our new split-merge procedures. We also show that given the same number of
samples, TSSB and treeCRP have similar ability to recover the subclonal
structure of a tumor.
Compressed Sensing with Very Sparse Gaussian Random Projections
Authors: Ping Li, Cun-Hui Zhang
Date: 2014-08-11 00:00:00
Tasks: compressed sensing

We study the use of very sparse random projections for compressed sensing
(sparse signal recovery) when the signal entries can be either positive or
negative. In our setting, the entries of a Gaussian design matrix are randomly
sparsified so that only a very small fraction of the entries are nonzero. Our
proposed decoding algorithm is simple and efficient in that the major cost is
one linear scan of the coordinates. We have developed two estimators: (i) the
{\em tie estimator}, and (ii) the {\em absolute minimum estimator}. Using only
the tie estimator, we are able to recover a $K$-sparse signal of length $N$
using $1.551 eK \log K/\delta$ measurements (where $\delta\leq 0.05$ is the
confidence). Using only the absolute minimum estimator, we can detect the
support of the signal using $eK\log N/\delta$ measurements. For a particular
coordinate, the absolute minimum estimator requires fewer measurements (i.e.,
with a constant $e$ instead of $1.551e$). Thus, the two estimators can be
combined to form an even more practical decoding framework.
  Prior studies have shown that existing one-scan (or roughly one-scan)
recovery algorithms using sparse matrices would require substantially more
(e.g., one order of magnitude) measurements than L1 decoding by linear
programming, when the nonzero entries of signals can be either negative or
positive. In this paper, following a known experimental setup, we show that, at
the same number of measurements, the recovery accuracies of our proposed method
are (at least) similar to the standard L1 decoding.
Robust computation of linear models by convex relaxation
Authors: Gilad Lerman, Michael McCoy, Joel A. Tropp, Teng Zhang
Date: 2012-02-18 00:00:00

Consider a dataset of vector-valued observations that consists of noisy
inliers, which are explained well by a low-dimensional subspace, along with
some number of outliers. This work describes a convex optimization problem,
called REAPER, that can reliably fit a low-dimensional model to this type of
data. This approach parameterizes linear subspaces using orthogonal projectors,
and it uses a relaxation of the set of orthogonal projectors to reach the
convex formulation. The paper provides an efficient algorithm for solving the
REAPER problem, and it documents numerical experiments which confirm that
REAPER can dependably find linear structure in synthetic and natural data. In
addition, when the inliers lie near a low-dimensional subspace, there is a
rigorous theory that describes when REAPER can approximate this subspace.
Learning to see like children: proof of concept
Authors: Marco Gori, Marco Lippi, Marco Maggini, Stefano Melacci
Date: 2014-08-11 00:00:00
Tasks: Lifelong learning

In the last few years we have seen a growing interest in machine learning
approaches to computer vision and, especially, to semantic labeling. Nowadays
state of the art systems use deep learning on millions of labeled images with
very successful results on benchmarks, though it is unlikely to expect similar
results in unrestricted visual environments. Most learning schemes essentially
ignore the inherent sequential structure of videos: this might be a critical
issue, since any visual recognition process is remarkably more complex when
shuffling video frames. Based on this remark, we propose a re-foundation of the
communication protocol between visual agents and the environment, which is
referred to as learning to see like children. Like for human interaction,
visual concepts are acquired by the agents solely by processing their own
visual stream along with human supervisions on selected pixels. We give a proof
of concept that remarkable semantic labeling can emerge within this protocol by
using only a few supervised examples. This is made possible by exploiting a
constraint of motion coherent labeling that virtually offers tons of
supervisions. Additional visual constraints, including those associated with
object supervisions, are used within the context of learning from constraints.
The framework is extended in the direction of lifelong learning, so as our
visual agents live in their own visual environment without distinguishing
learning and test set. Learning takes place in deep architectures under a
progressive developmental scheme. In order to evaluate our Developmental Visual
Agents (DVAs), in addition to classic benchmarks, we open the doors of our lab,
allowing people to evaluate DVAs by crowd-sourcing. Such assessment mechanism
might result in a paradigm shift in methodologies and algorithms for computer
vision, encouraging truly novel solutions within the proposed framework.
Optimizing Component Combination in a Multi-Indexing Paragraph Retrieval System
Authors: Boris Iolis, Gianluca Bontempi
Date: 2014-08-11 00:00:00
Tasks: Question Answering, Retrieval

We demonstrate a method to optimize the combination of distinct components in
a paragraph retrieval system. Our system makes use of several indices, query
generators and filters, each of them potentially contributing to the quality of
the returned list of results. The components are combined with a weighed sum,
and we optimize the weights using a heuristic optimization algorithm. This
allows us to maximize the quality of our results, but also to determine which
components are most valuable in our system. We evaluate our approach on the
paragraph selection task of a Question Answering dataset.
Video Face Editing Using Temporal-Spatial-Smooth Warping
Authors: Xiaoyan Li, DaCheng Tao
Date: 2014-08-11 00:00:00

Editing faces in videos is a popular yet challenging aspect of computer
vision and graphics, which encompasses several applications including facial
attractiveness enhancement, makeup transfer, face replacement, and expression
manipulation. Simply applying image-based warping algorithms to video-based
face editing produces temporal incoherence in the synthesized videos because it
is impossible to consistently localize facial features in two frames
representing two different faces in two different videos (or even two
consecutive frames representing the same face in one video). Therefore, high
performance face editing usually requires significant manual manipulation. In
this paper we propose a novel temporal-spatial-smooth warping (TSSW) algorithm
to effectively exploit the temporal information in two consecutive frames, as
well as the spatial smoothness within each frame. TSSW precisely estimates two
control lattices in the horizontal and vertical directions respectively from
the corresponding control lattices in the previous frame, by minimizing a novel
energy function that unifies a data-driven term, a smoothness term, and feature
point constraints. Corresponding warping surfaces then precisely map source
frames to the target frames. Experimental testing on facial attractiveness
enhancement, makeup transfer, face replacement, and expression manipulation
demonstrates that the proposed approaches can effectively preserve spatial
smoothness and temporal coherence in editing facial geometry, skin detail,
identity, and expression, which outperform the existing face editing methods.
In particular, TSSW is robust to subtly inaccurate localization of feature
points and is a vast improvement over image-based warping methods.
On the Complexity of Bandit Linear Optimization
Authors: Ohad Shamir
Date: 2014-08-11 00:00:00

We study the attainable regret for online linear optimization problems with
bandit feedback, where unlike the full-information setting, the player can only
observe its own loss rather than the full loss vector. We show that the price
of bandit information in this setting can be as large as $d$, disproving the
well-known conjecture that the regret for bandit linear optimization is at most
$\sqrt{d}$ times the full-information regret. Surprisingly, this is shown using
"trivial" modifications of standard domains, which have no effect in the
full-information setting. This and other results we present highlight some
interesting differences between full-information and bandit learning, which
were not considered in previous literature.
$OntoMath^{PRO}$ Ontology: A Linked Data Hub for Mathematics
Authors: Olga Nevzorova, Nikita Zhiltsov, Alexander Kirillovich, Evgeny Lipachev
Date: 2014-07-17 00:00:00
Tasks: Math

In this paper, we present an ontology of mathematical knowledge concepts that
covers a wide range of the fields of mathematics and introduces a balanced
representation between comprehensive and sensible models. We demonstrate the
applications of this representation in information extraction, semantic search,
and education. We argue that the ontology can be a core of future integration
of math-aware data sets in the Web of Data and, therefore, provide mappings
onto relevant datasets, such as DBpedia and ScienceWISE.
Physical Computing With No Clock to Implement the Gaussian Pyramid of SIFT Algorithm
Authors: Yi Li, Qi Wei, Fei Qiao, Huazhong Yang
Date: 2014-08-11 00:00:00
Tasks: GPU

Physical computing is a technology utilizing the nature of electronic devices
and circuit topology to cope with computing tasks. In this paper, we propose an
active circuit network to implement multi-scale Gaussian filter, which is also
called Gaussian Pyramid in image preprocessing. Various kinds of methods have
been tried to accelerate the key stage in image feature extracting algorithm
these years. Compared with existing technologies, GPU parallel computing and
FPGA accelerating technology, physical computing has great advantage on
processing speed as well as power consumption. We have verified that processing
time to implement the Gaussian pyramid of the SIFT algorithm stands on
nanosecond level through the physical computing technology, while other
existing methods all need at least hundreds of millisecond. With an estimate on
the stray capacitance of the circuit, the power consumption is around 670pJ to
filter a 256x256 image. To the best of our knowledge, this is the most fast
processing technology to accelerate the SIFT algorithm, and it is also a rather
energy-efficient method, thanks to the proposed physical computing technology.
Genetic Programming for Smart Phone Personalisation
Authors: Philip Valencia, Aiden Haak, Alban Cotillon, Raja Jurdak
Date: 2014-08-11 00:00:00

Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.
In principle determination of generic priors
Authors: Cael L. Hasse
Date: 2014-08-11 00:00:00

Probability theory as extended logic is completed such that essentially any
probability may be determined. This is done by considering propositional logic
(as opposed to predicate logic) as syntactically suffcient and imposing a
symmetry from propositional logic. It is shown how the notions of `possibility'
and `property' may be suffciently represented in propositional logic such that
1) the principle of indifference drops out and becomes essentially combinatoric
in nature and 2) one may appropriately represent assumptions where one assumes
there is a space of possibilities but does not assume the size of the space.
Modeling Creativity: Case Studies in Python
Authors: Tom De Smedt
Date: 2014-08-10 00:00:00

Modeling Creativity (doctoral dissertation, 2013) explores how creativity can
be represented using computational approaches. Our aim is to construct computer
models that exhibit creativity in an artistic context, that is, that are
capable of generating or evaluating an artwork (visual or linguistic), an
interesting new idea, a subjective opinion. The research was conducted in
2008-2012 at the Computational Linguistics Research Group (CLiPS, University of
Antwerp) under the supervision of Prof. Walter Daelemans. Prior research was
also conducted at the Experimental Media Research Group (EMRG, St. Lucas
University College of Art & Design Antwerp) under the supervision of Lucas
Nijs.
  Modeling Creativity examines creativity in a number of different
perspectives: from its origins in nature, which is essentially blind, to humans
and machines, and from generating creative ideas to evaluating and learning
their novelty and usefulness. We will use a hands-on approach with case studies
and examples in the Python programming language.
Exponentiated Gradient Exploration for Active Learning
Authors: Djallel Bouneffouf
Date: 2014-08-10 00:00:00
Tasks: Active Learning, General Classification

Active learning strategies respond to the costly labelling task in a
supervised classification by selecting the most useful unlabelled examples in
training a predictive model. Many conventional active learning algorithms focus
on refining the decision boundary, rather than exploring new regions that can
be more informative. In this setting, we propose a sequential algorithm named
EG-Active that can improve any Active learning algorithm by an optimal random
exploration. Experimental results show a statistically significant and
appreciable improvement in the performance of our new approach over the
existing active feedback methods.
R-UCB: a Contextual Bandit Algorithm for Risk-Aware Recommender Systems
Authors: Djallel Bouneffouf
Date: 2014-08-10 00:00:00
Tasks: Recommendation Systems, Reinforcement Learning

Mobile Context-Aware Recommender Systems can be naturally modelled as an
exploration/exploitation trade-off (exr/exp) problem, where the system has to
choose between maximizing its expected rewards dealing with its current
knowledge (exploitation) and learning more about the unknown user's preferences
to improve its knowledge (exploration). This problem has been addressed by the
reinforcement learning community but they do not consider the risk level of the
current user's situation, where it may be dangerous to recommend items the user
may not desire in her current situation if the risk level is high. We introduce
in this paper an algorithm named R-UCB that considers the risk level of the
user's situation to adaptively balance between exr and exp. The detailed
analysis of the experimental results reveals several important discoveries in
the exr/exp behaviour.