pwc-archive/papers-with-abstracts huggingface dataset:
Map Matching based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories
Authors: Xu Ming, Du Yi-man, Wu Jian-ping, Zhou Yang
Date: 2014-10-16 00:00:00

In order to improve offline map matching accuracy of low-sampling-rate GPS, a
map matching algorithm based on conditional random fields (CRF) and route
preference mining is proposed. In this algorithm, road offset distance and the
temporal-spatial relationship between the sampling points are used as features
of GPS trajectory in CRF model, which can utilize the advantages of integrating
the context information into features flexibly. When the sampling rate is too
low, it is difficult to guarantee the effectiveness using temporal-spatial
context modeled in CRF, and route preference of a driver is used as
replenishment to be superposed on the temporal-spatial transition features. The
experimental results show that this method can improve the accuracy of the
matching, especially in the case of low sampling rate.
A Semidefinite Programming Based Search Strategy for Feature Selection with Mutual Information Measure
Authors: Tofigh Naghibi, Sarah Hoffmann, Beat Pfister
Date: 2014-09-25 00:00:00
Tasks: feature selection

Feature subset selection, as a special case of the general subset selection
problem, has been the topic of a considerable number of studies due to the
growing importance of data-mining applications. In the feature subset selection
problem there are two main issues that need to be addressed: (i) Finding an
appropriate measure function than can be fairly fast and robustly computed for
high-dimensional data. (ii) A search strategy to optimize the measure over the
subset space in a reasonable amount of time. In this article mutual information
between features and class labels is considered to be the measure function. Two
series expansions for mutual information are proposed, and it is shown that
most heuristic criteria suggested in the literature are truncated
approximations of these expansions. It is well-known that searching the whole
subset space is an NP-hard problem. Here, instead of the conventional
sequential search algorithms, we suggest a parallel search strategy based on
semidefinite programming (SDP) that can search through the subset space in
polynomial time. By exploiting the similarities between the proposed algorithm
and an instance of the maximum-cut problem in graph theory, the approximation
ratio of this algorithm is derived and is compared with the approximation ratio
of the backward elimination method. The experiments show that it can be
misleading to judge the quality of a measure solely based on the classification
accuracy, without taking the effect of the non-optimum search strategy into
account.
On Coarse Graining of Information and Its Application to Pattern Recognition
Authors: Ali Ghaderi
Date: 2014-11-12 00:00:00

We propose a method based on finite mixture models for classifying a set of
observations into number of different categories. In order to demonstrate the
method, we show how the component densities for the mixture model can be
derived by using the maximum entropy method in conjunction with conservation of
Pythagorean means. Several examples of distributions belonging to the
Pythagorean family are derived. A discussion on estimation of model parameters
and the number of categories is also given.
Distributed Representations for Compositional Semantics
Authors: Karl Moritz Hermann
Date: 2014-11-12 00:00:00
Tasks: Representation Learning

The mathematical representation of semantics is a key issue for Natural
Language Processing (NLP). A lot of research has been devoted to finding ways
of representing the semantics of individual words in vector spaces.
Distributional approaches --- meaning distributed representations that exploit
co-occurrence statistics of large corpora --- have proved popular and
successful across a number of tasks. However, natural language usually comes in
structures beyond the word level, with meaning arising not only from the
individual words but also the structure they are contained in at the phrasal or
sentential level. Modelling the compositional process by which the meaning of
an utterance arises from the meaning of its parts is an equally fundamental
task of NLP.
  This dissertation explores methods for learning distributed semantic
representations and models for composing these into representations for larger
linguistic units. Our underlying hypothesis is that neural models are a
suitable vehicle for learning semantically rich representations and that such
representations in turn are suitable vehicles for solving important tasks in
natural language processing. The contribution of this thesis is a thorough
evaluation of our hypothesis, as part of which we introduce several new
approaches to representation learning and compositional semantics, as well as
multiple state-of-the-art models which apply distributed semantic
representations to various tasks in NLP.
Exact Estimation of Multiple Directed Acyclic Graphs
Authors: Chris. J. Oates, Jim Q. Smith, Sach Mukherjee, James Cussens
Date: 2014-04-04 00:00:00

This paper considers the problem of estimating the structure of multiple
related directed acyclic graph (DAG) models. Building on recent developments in
exact estimation of DAGs using integer linear programming (ILP), we present an
ILP approach for joint estimation over multiple DAGs, that does not require
that the vertices in each DAG share a common ordering. Furthermore, we allow
also for (potentially unknown) dependency structure between the DAGs. Results
are presented on both simulated data and fMRI data obtained from multiple
subjects.
Collecting Image Description Datasets using Crowdsourcing
Authors: Ramakrishna Vedantam, C. Lawrence Zitnick, Devi Parikh
Date: 2014-11-12 00:00:00
Tasks: Image Description, Sentence

We describe our two new datasets with images described by humans. Both the
datasets were collected using Amazon Mechanical Turk, a crowdsourcing platform.
The two datasets contain significantly more descriptions per image than other
existing datasets. One is based on a popular image description dataset called
the UIUC Pascal Sentence Dataset, whereas the other is based on the Abstract
Scenes dataset con- taining images made from clipart objects. In this paper we
describe our interfaces, analyze some properties of and show example
descriptions from our two datasets.
Projecting Markov Random Field Parameters for Fast Mixing
Authors: Xianghang Liu, Justin Domke
Date: 2014-11-05 00:00:00
Proceeding: NeurIPS 2014 12

Markov chain Monte Carlo (MCMC) algorithms are simple and extremely powerful
techniques to sample from almost arbitrary distributions. The flaw in practice
is that it can take a large and/or unknown amount of time to converge to the
stationary distribution. This paper gives sufficient conditions to guarantee
that univariate Gibbs sampling on Markov Random Fields (MRFs) will be fast
mixing, in a precise sense. Further, an algorithm is given to project onto this
set of fast-mixing parameters in the Euclidean norm. Following recent work, we
give an example use of this to project in various divergence measures,
comparing univariate marginals obtained by sampling after projection to common
variational methods and Gibbs sampling on the original parameters.
Bounded Regret for Finite-Armed Structured Bandits
Authors: Tor Lattimore, Remi Munos
Date: 2014-11-11 00:00:00
Proceeding: NeurIPS 2014 12

We study a new type of K-armed bandit problem where the expected return of
one arm may depend on the returns of other arms. We present a new algorithm for
this general class of problems and show that under certain circumstances it is
possible to achieve finite expected cumulative regret. We also give
problem-dependent lower bounds on the cumulative regret showing that at least
in special cases the new algorithm is nearly optimal.
Marginal Pseudo-Likelihood Learning of Markov Network structures
Authors: Johan Pensar, Henrik Nyman, Juha Niiranen, Jukka Corander
Date: 2014-01-20 00:00:00

Undirected graphical models known as Markov networks are popular for a wide
variety of applications ranging from statistical physics to computational
biology. Traditionally, learning of the network structure has been done under
the assumption of chordality which ensures that efficient scoring methods can
be used. In general, non-chordal graphs have intractable normalizing constants
which renders the calculation of Bayesian and other scores difficult beyond
very small-scale systems. Recently, there has been a surge of interest towards
the use of regularized pseudo-likelihood methods for structural learning of
large-scale Markov network models, as such an approach avoids the assumption of
chordality. The currently available methods typically necessitate the use of a
tuning parameter to adapt the level of regularization for a particular dataset,
which can be optimized for example by cross-validation. Here we introduce a
Bayesian version of pseudo-likelihood scoring of Markov networks, which enables
an automatic regularization through marginalization over the nuisance
parameters in the model. We prove consistency of the resulting MPL estimator
for the network structure via comparison with the pseudo information criterion.
Identification of the MPL-optimal network on a prescanned graph space is
considered with both greedy hill climbing and exact pseudo-Boolean optimization
algorithms. We find that for reasonable sample sizes the hill climbing approach
most often identifies networks that are at a negligible distance from the
restricted global optimum. Using synthetic and existing benchmark networks, the
marginal pseudo-likelihood method is shown to generally perform favorably
against recent popular inference methods for Markov networks.
Accelerating the ANT Colony Optimization By Smart ANTs, Using Genetic Operator
Authors: Hassan Ismkhan
Date: 2014-11-11 00:00:00

This paper research review Ant colony optimization (ACO) and Genetic
Algorithm (GA), both are two powerful meta-heuristics. This paper explains some
major defects of these two algorithm at first then proposes a new model for ACO
in which, artificial ants use a quick genetic operator and accelerate their
actions in selecting next state. Experimental results show that proposed hybrid
algorithm is effective and its performance including speed and accuracy beats
other version.
Logical Limitations to Machine Ethics with Consequences to Lethal Autonomous Weapons
Authors: Matthias Englert, Sandra Siebert, Martin Ziegler
Date: 2014-11-11 00:00:00
Tasks: Ethics

Lethal Autonomous Weapons promise to revolutionize warfare -- and raise a
multitude of ethical and legal questions. It has thus been suggested to program
values and principles of conduct (such as the Geneva Conventions) into the
machines' control, thereby rendering them both physically and morally superior
to human combatants.
  We employ mathematical logic and theoretical computer science to explore
fundamental limitations to the moral behaviour of intelligent machines in a
series of "Gedankenexperiments": Refining and sharpening variants of the
Trolley Problem leads us to construct an (admittedly artificial but) fully
deterministic situation where a robot is presented with two choices: one
morally clearly preferable over the other -- yet, based on the undecidability
of the Halting problem, it provably cannot decide algorithmically which one.
Our considerations have surprising implications to the question of
responsibility and liability for an autonomous system's actions and lead to
specific technical recommendations.
Detecting Figures and Part Labels in Patents: Competition-Based Development of Image Processing Algorithms
Authors: Christoph Riedl, Richard Zanibbi, Marti A. Hearst, Siyu Zhu, Michael Menietti, Jason Crusan, Ivan Metelsky, Karim R. Lakhani
Date: 2014-10-24 00:00:00

We report the findings of a month-long online competition in which
participants developed algorithms for augmenting the digital version of patent
documents published by the United States Patent and Trademark Office (USPTO).
The goal was to detect figures and part labels in U.S. patent drawing pages.
The challenge drew 232 teams of two, of which 70 teams (30%) submitted
solutions. Collectively, teams submitted 1,797 solutions that were compiled on
the competition servers. Participants reported spending an average of 63 hours
developing their solutions, resulting in a total of 5,591 hours of development
time. A manually labeled dataset of 306 patents was used for training, online
system tests, and evaluation. The design and performance of the top-5 systems
are presented, along with a system developed after the competition which
illustrates that winning teams produced near state-of-the-art results under
strict time and computation constraints. For the 1st place system, the harmonic
mean of recall and precision (f-measure) was 88.57% for figure region
detection, 78.81% for figure regions with correctly recognized figure titles,
and 70.98% for part label detection and character recognition. Data and
software from the competition are available through the online UCI Machine
Learning repository to inspire follow-on work by the image processing
community.
Turn Down that Noise: Synaptic Encoding of Afferent SNR in a Single Spiking Neuron
Authors: Saeed Afshar, Libin George, Jonathan Tapson, Andre van Schaik, Philip de Chazal, Tara Julia Hamilton
Date: 2014-11-11 00:00:00

We have added a simplified neuromorphic model of Spike Time Dependent
Plasticity (STDP) to the Synapto-dendritic Kernel Adapting Neuron (SKAN). The
resulting neuron model is the first to show synaptic encoding of afferent
signal to noise ratio in addition to the unsupervised learning of spatio
temporal spike patterns. The neuron model is particularly suitable for
implementation in digital neuromorphic hardware as it does not use any complex
mathematical operations and uses a novel approach to achieve synaptic
homeostasis. The neurons noise compensation properties are characterized and
tested on noise corrupted zeros digits of the MNIST handwritten dataset.
Results show the simultaneously learning common patterns in its input data
while dynamically weighing individual afferent channels based on their signal
to noise ratio. Despite its simplicity the interesting behaviors of the neuron
model and the resulting computational power may offer insights into biological
systems.
Supervised Classification of Flow Cytometric Samples via the Joint Clustering and Matching (JCM) Procedure
Authors: Sharon X. Lee, Geoffrey J. McLachlan, Saumyadipta Pyne
Date: 2014-11-11 00:00:00
Tasks: Clustering, General Classification

We consider the use of the Joint Clustering and Matching (JCM) procedure for
the supervised classification of a flow cytometric sample with respect to a
number of predefined classes of such samples. The JCM procedure has been
proposed as a method for the unsupervised classification of cells within a
sample into a number of clusters and in the case of multiple samples, the
matching of these clusters across the samples. The two tasks of clustering and
matching of the clusters are performed simultaneously within the JCM framework.
In this paper, we consider the case where there is a number of distinct classes
of samples whose class of origin is known, and the problem is to classify a new
sample of unknown class of origin to one of these predefined classes. For
example, the different classes might correspond to the types of a particular
disease or to the various health outcomes of a patient subsequent to a course
of treatment. We show and demonstrate on some real datasets how the JCM
procedure can be used to carry out this supervised classification task. A
mixture distribution is used to model the distribution of the expressions of a
fixed set of markers for each cell in a sample with the components in the
mixture model corresponding to the various populations of cells in the
composition of the sample. For each class of samples, a class template is
formed by the adoption of random-effects terms to model the inter-sample
variation within a class. The classification of a new unclassified sample is
undertaken by assigning the unclassified sample to the class that minimizes the
Kullback-Leibler distance between its fitted mixture density and each class
density provided by the class templates.
Speaker Identification From Youtube Obtained Data
Authors: Nitesh Kumar Chaudhary
Date: 2014-11-11 00:00:00
Tasks: parameter estimation, Quantization, Speaker Identification, Speaker Verification

An efficient, and intuitive algorithm is presented for the identification of
speakers from a long dataset (like YouTube long discussion, Cocktail party
recorded audio or video).The goal of automatic speaker identification is to
identify the number of different speakers and prepare a model for that speaker
by extraction, characterization and speaker-specific information contained in
the speech signal. It has many diverse application specially in the field of
Surveillance, Immigrations at Airport, cyber security, transcription in
multi-source of similar sound source, where it is difficult to assign
transcription arbitrary. The most commonly speech parametrization used in
speaker verification, K-mean, cepstral analysis, is detailed. Gaussian mixture
modeling, which is the speaker modeling technique is then explained. Gaussian
mixture models (GMM), perhaps the most robust machine learning algorithm has
been introduced examine and judge carefully speaker identification in text
independent. The application or employment of Gaussian mixture models for
monitoring & Analysing speaker identity is encouraged by the familiarity,
awareness, or understanding gained through experience that Gaussian spectrum
depict the characteristics of speaker's spectral conformational pattern and
remarkable ability of GMM to construct capricious densities after that we
illustrate 'Expectation maximization' an iterative algorithm which takes some
arbitrary value in initial estimation and carry on the iterative process until
the convergence of value is observed,so by doing various number of experiments
we are able to obtain 79 ~ 82% of identification rate using Vector quantization
and 85 ~ 92.6% of identification rate using GMM modeling by Expectation
maximization parameter estimation depending on variation of parameter.
Warranty Cost Estimation Using Bayesian Network
Authors: Karamjit Singh, Puneet Agarwal, Gautam Shroff
Date: 2014-11-11 00:00:00
Tasks: Diagnostic

All multi-component product manufacturing companies face the problem of
warranty cost estimation. Failure rate analysis of components plays a key role
in this problem. Data source used for failure rate analysis has traditionally
been past failure data of components. However, failure rate analysis can be
improved by means of fusion of additional information, such as symptoms
observed during after-sale service of the product, geographical information
(hilly or plains areas), and information from tele-diagnostic analytics. In
this paper, we propose an approach, which learns dependency between
part-failures and symptoms gleaned from such diverse sources of information, to
predict expected number of failures with better accuracy. We also indicate how
the optimum warranty period can be computed. We demonstrate, through empirical
results, that our method can improve the warranty cost estimates significantly.
The HAWKwood Database
Authors: Christopher Herbon
Date: 2014-10-16 00:00:00

We present a database consisting of wood pile images, which can be used as a
benchmark to evaluate the performance of wood pile detection and surveying
algorithms. We distinguish six database cate- gories which can be used for
different types of algorithms. Images of real and synthetic scenes are
provided, which consist of 7655 images divided into 354 data sets. Depending on
the category the data sets either include ground truth data or forestry
specific measurements with which algorithms may be compared.
Inferring User Preferences by Probabilistic Logical Reasoning over Social Networks
Authors: Jiwei Li, Alan Ritter, Dan Jurafsky
Date: 2014-11-11 00:00:00
Tasks: Attribute, Logical Reasoning, Relation Extraction

We propose a framework for inferring the latent attitudes or preferences of
users by performing probabilistic first-order logical reasoning over the social
network graph. Our method answers questions about Twitter users like {\em Does
this user like sushi?} or {\em Is this user a New York Knicks fan?} by building
a probabilistic model that reasons over user attributes (the user's location or
gender) and the social network (the user's friends and spouse), via inferences
like homophily (I am more likely to like sushi if spouse or friends like sushi,
I am more likely to like the Knicks if I live in New York). The algorithm uses
distant supervision, semi-supervised data harvesting and vector space models to
extract user attributes (e.g. spouse, education, location) and preferences
(likes and dislikes) from text. The extracted propositions are then fed into a
probabilistic reasoner (we investigate both Markov Logic and Probabilistic Soft
Logic). Our experiments show that probabilistic logical reasoning significantly
improves the performance on attribute and relation extraction, and also
achieves an F-score of 0.791 at predicting a users likes or dislikes,
significantly better than two strong baselines.
Recursive Total Least-Squares Algorithm Based on Inverse Power Method and Dichotomous Coordinate-Descent Iterations
Authors: Reza Arablouei, Kutluyıl Doğançay, Stefan Werner
Date: 2014-08-25 00:00:00

We develop a recursive total least-squares (RTLS) algorithm for
errors-in-variables system identification utilizing the inverse power method
and the dichotomous coordinate-descent (DCD) iterations. The proposed
algorithm, called DCD-RTLS, outperforms the previously-proposed RTLS
algorithms, which are based on the line-search method, with reduced
computational complexity. We perform a comprehensive analysis of the DCD-RTLS
algorithm and show that it is asymptotically unbiased as well as being stable
in the mean. We also find a lower bound for the forgetting factor that ensures
mean-square stability of the algorithm and calculate the theoretical
steady-state mean-square deviation (MSD). We verify the effectiveness of the
proposed algorithm and the accuracy of the predicted steady-state MSD via
simulations.
Non-crossing dependencies: least effort, not grammar
Authors: Ramon Ferrer-i-Cancho
Date: 2014-11-10 00:00:00

The use of null hypotheses (in a statistical sense) is common in hard
sciences but not in theoretical linguistics. Here the null hypothesis that the
low frequency of syntactic dependency crossings is expected by an arbitrary
ordering of words is rejected. It is shown that this would require star
dependency structures, which are both unrealistic and too restrictive. The
hypothesis of the limited resources of the human brain is revisited. Stronger
null hypotheses taking into account actual dependency lengths for the
likelihood of crossings are presented. Those hypotheses suggests that crossings
are likely to reduce when dependencies are shortened. A hypothesis based on
pressure to reduce dependency lengths is more parsimonious than a principle of
minimization of crossings or a grammatical ban that is totally dissociated from
the general and non-linguistic principle of economy.
Bounding the Probability of Causation in Mediation Analysis
Authors: A. P. Dawid, R. Murtas, M. Musio
Date: 2014-11-10 00:00:00
Tasks: Causal Inference

Given empirical evidence for the dependence of an outcome variable on an
exposure variable, we can typically only provide bounds for the "probability of
causation" in the case of an individual who has developed the outcome after
being exposed. We show how these bounds can be adapted or improved if further
information becomes available. In addition to reviewing existing work on this
topic, we provide a new analysis for the case where a mediating variable can be
observed. In particular we show how the probability of causation can be bounded
when there is no direct effect and no confounding.
  Keywords: Causal inference, Mediation Analysis, Probability of Causation