pwc-archive/papers-with-abstracts huggingface dataset:
Online Bayesian Passive-Aggressive Learning
Authors: Tianlin Shi, Jun Zhu
Date: 2013-12-12 00:00:00
Tasks: Bayesian Inference, Descriptive, Topic Models

Online Passive-Aggressive (PA) learning is an effective framework for
performing max-margin online learning. But the deterministic formulation and
estimated single large-margin model could limit its capability in discovering
descriptive structures underlying complex data. This pa- per presents online
Bayesian Passive-Aggressive (BayesPA) learning, which subsumes the online PA
and extends naturally to incorporate latent variables and perform nonparametric
Bayesian inference, thus providing great flexibility for explorative analysis.
We apply BayesPA to topic modeling and derive efficient online learning
algorithms for max-margin topic models. We further develop nonparametric
methods to resolve the number of topics. Experimental results on real datasets
show that our approaches significantly improve time efficiency while
maintaining comparable results with the batch counterparts.
Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic
Authors: James Sharpnack, Akshay Krishnamurthy, Aarti Singh
Date: 2013-12-11 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Anomaly Detection

The detection of anomalous activity in graphs is a statistical problem that
arises in many applications, such as network surveillance, disease outbreak
detection, and activity monitoring in social networks. Beyond its wide
applicability, graph structured anomaly detection serves as a case study in the
difficulty of balancing computational complexity with statistical power. In
this work, we develop from first principles the generalized likelihood ratio
test for determining if there is a well connected region of activation over the
vertices in the graph in Gaussian noise. Because this test is computationally
infeasible, we provide a relaxation, called the Lovasz extended scan statistic
(LESS) that uses submodularity to approximate the intractable generalized
likelihood ratio. We demonstrate a connection between LESS and maximum
a-posteriori inference in Markov random fields, which provides us with a
poly-time algorithm for LESS. Using electrical network theory, we are able to
control type 1 error for LESS and prove conditions under which LESS is risk
consistent. Finally, we consider specific graph models, the torus, k-nearest
neighbor graphs, and epsilon-random graphs. We show that on these graphs our
results provide near-optimal performance by matching our results to known lower
bounds.
Implicit Sensitive Text Summarization based on Data Conveyed by Connectives
Authors: Henda Chorfi Ouertani
Date: 2013-12-11 00:00:00
Tasks: Text Summarization, World Knowledge

So far and trying to reach human capabilities, research in automatic
summarization has been based on hypothesis that are both enabling and limiting.
Some of these limitations are: how to take into account and reflect (in the
generated summary) the implicit information conveyed in the text, the author
intention, the reader intention, the context influence, the general world
knowledge. Thus, if we want machines to mimic human abilities, then they will
need access to this same large variety of knowledge. The implicit is affecting
the orientation and the argumentation of the text and consequently its summary.
Most of Text Summarizers (TS) are processing as compressing the initial data
and they necessarily suffer from information loss. TS are focusing on features
of the text only, not on what the author intended or why the reader is reading
the text. In this paper, we address this problem and we present a system
focusing on acquiring knowledge that is implicit. We principally spotlight the
implicit information conveyed by the argumentative connectives such as: but,
even, yet and their effect on the summary.
Towards The Development of a Bishnupriya Manipuri Corpus
Authors: Nayan Jyoti Kalita, Navanath Saharia, Smriti Kumar Sinha
Date: 2013-12-11 00:00:00

For any deep computational processing of language we need evidences, and one
such set of evidences is corpus. This paper describes the development of a
text-based corpus for the Bishnupriya Manipuri language. A Corpus is considered
as a building block for any language processing tasks. Due to the lack of
awareness like other Indian languages, it is also studied less frequently. As a
result the language still lacks a good corpus and basic language processing
tools. As per our knowledge this is the first effort to develop a corpus for
Bishnupriya Manipuri language.
Thickness Mapping of Eleven Retinal Layers in Normal Eyes Using Spectral Domain Optical Coherence Tomography
Authors: Raheleh Kafieh, Hossein Rabbani, Fedra Hajizadeh, Michael D. Abramoff, Milan Sonka
Date: 2013-12-11 00:00:00

Purpose. This study was conducted to determine the thickness map of eleven
retinal layers in normal subjects by spectral domain optical coherence
tomography (SD-OCT) and evaluate their association with sex and age. Methods.
Mean regional retinal thickness of 11 retinal layers were obtained by automatic
three-dimensional diffusion-map-based method in 112 normal eyes of 76 Iranian
subjects. Results. The thickness map of central foveal area in layer 1, 3, and
4 displayed the minimum thickness (P<0.005 for all). Maximum thickness was
observed in nasal to the fovea of layer 1 (P<0.001) and in a circular pattern
in the parafoveal retinal area of layers 2, 3 and 4 and in central foveal area
of layer 6 (P<0.001). Temporal and inferior quadrants of the total retinal
thickness and most of other quadrants of layer 1 were significantly greater in
the men than in the women. Surrounding eight sectors of total retinal thickness
and a limited number of sectors in layer 1 and 4 significantly correlated with
age. Conclusion. SD-OCT demonstrated the three-dimensional thickness
distribution of retinal layers in normal eyes. Thickness of layers varied with
sex and age and in different sectors. These variables should be considered
while evaluating macular thickness.
Semantic Types, Lexical Sorts and Classifiers
Authors: Bruno Mery, Christian Retoré
Date: 2013-12-11 00:00:00

We propose a cognitively and linguistically motivated set of sorts for
lexical semantics in a compositional setting: the classifiers in languages that
do have such pronouns. These sorts are needed to include lexical considerations
in a semantical analyser such as Boxer or Grail. Indeed, all proposed lexical
extensions of usual Montague semantics to model restriction of selection,
felicitous and infelicitous copredication require a rich and refined type
system whose base types are the lexical sorts, the basis of the many-sorted
logic in which semantical representations of sentences are stated. However,
none of those approaches define precisely the actual base types or sorts to be
used in the lexicon. In this article, we shall discuss some of the options
commonly adopted by researchers in formal lexical semantics, and defend the
view that classifiers in the languages which have such pronouns are an
appealing solution, both linguistically and cognitively motivated.
Fast Neighborhood Graph Search using Cartesian Concatenation
Authors: Jingdong Wang, Jing Wang, Gang Zeng, Rui Gan, Shipeng Li, Baining Guo
Date: 2013-12-11 00:00:00

In this paper, we propose a new data structure for approximate nearest
neighbor search. This structure augments the neighborhood graph with a bridge
graph. We propose to exploit Cartesian concatenation to produce a large set of
vectors, called bridge vectors, from several small sets of subvectors. Each
bridge vector is connected with a few reference vectors near to it, forming a
bridge graph. Our approach finds nearest neighbors by simultaneously traversing
the neighborhood graph and the bridge graph in the best-first strategy. The
success of our approach stems from two factors: the exact nearest neighbor
search over a large number of bridge vectors can be done quickly, and the
reference vectors connected to a bridge (reference) vector near the query are
also likely to be near the query. Experimental results on searching over large
scale datasets (SIFT, GIST and HOG) show that our approach outperforms
state-of-the-art ANN search algorithms in terms of efficiency and accuracy. The
combination of our approach with the IVFADC system also shows superior
performance over the BIGANN dataset of $1$ billion SIFT features compared with
the best previously published result.
Fast Approximate $K$-Means via Cluster Closures
Authors: Jingdong Wang, Jing Wang, Qifa Ke, Gang Zeng, Shipeng Li
Date: 2013-12-11 00:00:00
Tasks: Clustering, Image Retrieval, Retrieval

$K$-means, a simple and effective clustering algorithm, is one of the most
widely used algorithms in multimedia and computer vision community. Traditional
$k$-means is an iterative algorithm---in each iteration new cluster centers are
computed and each data point is re-assigned to its nearest center. The cluster
re-assignment step becomes prohibitively expensive when the number of data
points and cluster centers are large.
  In this paper, we propose a novel approximate $k$-means algorithm to greatly
reduce the computational complexity in the assignment step. Our approach is
motivated by the observation that most active points changing their cluster
assignments at each iteration are located on or near cluster boundaries. The
idea is to efficiently identify those active points by pre-assembling the data
into groups of neighboring points using multiple random spatial partition
trees, and to use the neighborhood information to construct a closure for each
cluster, in such a way only a small number of cluster candidates need to be
considered when assigning a data point to its nearest cluster. Using complexity
analysis, image data clustering, and applications to image retrieval, we show
that our approach out-performs state-of-the-art approximate $k$-means
algorithms in terms of clustering quality and efficiency.
Representing Knowledge Base into Database for WAP and Web-based Expert System
Authors: Istiadi, Emma Budi Sulistiarini
Date: 2013-12-11 00:00:00

Expert System is developed as consulting service for users spread or public
requires affordable access. The Internet has become a medium for such services,
but presence of mobile devices make the access becomes more widespread by
utilizing mobile web and WAP (Wireless Application Protocol). Applying expert
systems applications over the web and WAP requires a knowledge base
representation that can be accessed simultaneously. This paper proposes single
database to accommodate the knowledge representation with decision tree mapping
approach. Because of the database exist, consulting application through both
web and WAP can access it to provide expert system services options for more
affordable for public.
Heat kernel coupling for multiple graph analysis
Authors: Michael M. Bronstein, Klaus Glashoff
Date: 2013-12-11 00:00:00

In this paper, we introduce heat kernel coupling (HKC) as a method of
constructing multimodal spectral geometry on weighted graphs of different size
without vertex-wise bijective correspondence. We show that Laplacian averaging
can be derived as a limit case of HKC, and demonstrate its applications on
several problems from the manifold learning and pattern recognition domain.
Sparse Allreduce: Efficient Scalable Communication for Power-Law Data
Authors: Huasha Zhao, John Canny
Date: 2013-12-11 00:00:00
Tasks: Clustering, Topic Models

Many large datasets exhibit power-law statistics: The web graph, social
networks, text data, click through data etc. Their adjacency graphs are termed
natural graphs, and are known to be difficult to partition. As a consequence
most distributed algorithms on these graphs are communication intensive. Many
algorithms on natural graphs involve an Allreduce: a sum or average of
partitioned data which is then shared back to the cluster nodes. Examples
include PageRank, spectral partitioning, and many machine learning algorithms
including regression, factor (topic) models, and clustering. In this paper we
describe an efficient and scalable Allreduce primitive for power-law data. We
point out scaling problems with existing butterfly and round-robin networks for
Sparse Allreduce, and show that a hybrid approach improves on both.
Furthermore, we show that Sparse Allreduce stages should be nested instead of
cascaded (as in the dense case). And that the optimum throughput Allreduce
network should be a butterfly of heterogeneous degree where degree decreases
with depth into the network. Finally, a simple replication scheme is introduced
to deal with node failures. We present experiments showing significant
improvements over existing systems such as PowerGraph and Hadoop.
Active Player Modelling
Authors: Julian Togelius, Noor Shaker, Georgios N. Yannakakis
Date: 2013-12-10 00:00:00
Tasks: Active Learning, Form

We argue for the use of active learning methods for player modelling. In
active learning, the learning algorithm chooses where to sample the search
space so as to optimise learning progress. We hypothesise that player modelling
based on active learning could result in vastly more efficient learning, but
will require big changes in how data is collected. Some example active player
modelling scenarios are described. A particular form of active learning is also
equivalent to an influential formalisation of (human and machine) curiosity,
and games with active learning could therefore be seen as being curious about
the player. We further hypothesise that this form of curiosity is symmetric,
and therefore that games that explore their players based on the principles of
active learning will turn out to select game configurations that are
interesting to the player that is being explored.
Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning
Authors: Mohammad H. Alomari, Aya Samaha, Khaled AlKamha
Date: 2013-12-10 00:00:00
Tasks: BIG-bench Machine Learning, Brain Computer Interface, EEG, Electroencephalogram (EEG), General Classification

In this paper, we propose an automated computer platform for the purpose of
classifying Electroencephalography (EEG) signals associated with left and right
hand movements using a hybrid system that uses advanced feature extraction
techniques and machine learning algorithms. It is known that EEG represents the
brain activity by the electrical voltage fluctuations along the scalp, and
Brain-Computer Interface (BCI) is a device that enables the use of the brain
neural activity to communicate with others or to control machines, artificial
limbs, or robots without direct physical movements. In our research work, we
aspired to find the best feature extraction method that enables the
differentiation between left and right executed fist movements through various
classification algorithms. The EEG dataset used in this research was created
and contributed to PhysioNet by the developers of the BCI2000 instrumentation
system. Data was preprocessed using the EEGLAB MATLAB toolbox and artifacts
removal was done using AAR. Data was epoched on the basis of Event-Related (De)
Synchronization (ERD/ERS) and movement-related cortical potentials (MRCP)
features. Mu/beta rhythms were isolated for the ERD/ERS analysis and delta
rhythms were isolated for the MRCP analysis. The Independent Component Analysis
(ICA) spatial filter was applied on related channels for noise reduction and
isolation of both artifactually and neutrally generated EEG sources. The final
feature vector included the ERD, ERS, and MRCP features in addition to the
mean, power and energy of the activations of the resulting independent
components of the epoched feature datasets. The datasets were inputted into two
machine-learning algorithms: Neural Networks (NNs) and Support Vector Machines
(SVMs). Intensive experiments were carried out and optimum classification
performances of 89.8 and 97.1 were obtained using NN and SVM, respectively.
Wavelet and Fast Fourier Transform based analysis of Solar Image
Authors: Sabyasachi Mukhopadhyay, Debadatta Dash, Swapnil Barmase, Prasanta K. Panigrahi
Date: 2013-10-30 00:00:00

Both of Wavelet and Fast Fourier Transform are strong signal processing tools
in the field of Data Analysis. In this paper fast fourier transform (FFT) and
Wavelet Transform are employed to observe some important features of Solar
image (December, 2004). We have tried to find out the periodicity and coherence
of different sections of the solar image. We plotted the distribution of energy
in solar surface by analyzing the solar image with scalograms and
3D-coefficient plots.
Performance Analysis Of Neural Network Models For Oxazolines And Oxazoles Derivatives Descriptor Dataset
Authors: Doreswamy, Chanabasayya . M. Vastrad
Date: 2013-12-10 00:00:00
Tasks: Drug Discovery, Medical Diagnosis, quantile regression

Neural networks have been used successfully to a broad range of areas such as
business, data mining, drug discovery and biology. In medicine, neural networks
have been applied widely in medical diagnosis, detection and evaluation of new
drugs and treatment cost estimation. In addition, neural networks have begin
practice in data mining strategies for the aim of prediction, knowledge
discovery. This paper will present the application of neural networks for the
prediction and analysis of antitubercular activity of Oxazolines and Oxazoles
derivatives. This study presents techniques based on the development of Single
hidden layer neural network (SHLFFNN), Gradient Descent Back propagation neural
network (GDBPNN), Gradient Descent Back propagation with momentum neural
network (GDBPMNN), Back propagation with Weight decay neural network (BPWDNN)
and Quantile regression neural network (QRNN) of artificial neural network
(ANN) models Here, we comparatively evaluate the performance of five neural
network techniques. The evaluation of the efficiency of each model by ways of
benchmark experiments is an accepted application. Cross-validation and
resampling techniques are commonly used to derive point estimates of the
performances which are compared to identify methods with good properties.
Predictive accuracy was evaluated using the root mean squared error (RMSE),
Coefficient determination(???), mean absolute error(MAE), mean percentage
error(MPE) and relative square error(RSE). We found that all five neural
network models were able to produce feasible models. QRNN model is outperforms
with all statistical tests amongst other four models.
mARC: Memory by Association and Reinforcement of Contexts
Authors: Norbert Rimoux, Patrice Descourt
Date: 2013-12-10 00:00:00
Tasks: Artificial Life, Quantization, Retrieval

This paper introduces the memory by Association and Reinforcement of Contexts
(mARC). mARC is a novel data modeling technology rooted in the second
quantization formulation of quantum mechanics. It is an all-purpose incremental
and unsupervised data storage and retrieval system which can be applied to all
types of signal or data, structured or unstructured, textual or not. mARC can
be applied to a wide range of information clas-sification and retrieval
problems like e-Discovery or contextual navigation. It can also for-mulated in
the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast
to Conway approach, the objects evolve in a massively multidimensional space.
In order to start evaluating the potential of mARC we have built a mARC-based
Internet search en-gine demonstrator with contextual functionality. We compare
the behavior of the mARC demonstrator with Google search both in terms of
performance and relevance. In the study we find that the mARC search engine
demonstrator outperforms Google search by an order of magnitude in response
time while providing more relevant results for some classes of queries.
Explore or exploit? A generic model and an exactly solvable case
Authors: Thomas Gueudré, Alexander Dobrinevski, Jean-Philippe Bouchaud
Date: 2013-10-18 00:00:00

Finding a good compromise between the exploitation of known resources and the
exploration of unknown, but potentially more profitable choices, is a general
problem, which arises in many different scientific disciplines. We propose a
stylized model for these exploration-exploitation situations, including
population or economic growth, portfolio optimisation, evolutionary dynamics,
or the problem of optimal pinning of vortices or dislocations in disordered
materials. We find the exact growth rate of this model for tree-like geometries
and prove the existence of an optimal migration rate in this case. Numerical
simulations in the one-dimensional case confirm the generic existence of an
optimum.
OntoVerbal: a Generic Tool and Practical Application to SNOMED CT
Authors: Shao Fen Liang, Donia Scott, Robert Stevens, Alan Rector
Date: 2013-12-10 00:00:00
Tasks: Text Generation

Ontology development is a non-trivial task requiring expertise in the chosen
ontological language. We propose a method for making the content of ontologies
more transparent by presenting, through the use of natural language generation,
naturalistic descriptions of ontology classes as textual paragraphs. The method
has been implemented in a proof-of- concept system, OntoVerbal, that
automatically generates paragraph-sized textual descriptions of ontological
classes expressed in OWL. OntoVerbal has been applied to ontologies that can be
loaded into Prot\'eg\'e and been evaluated with SNOMED CT, showing that it
provides coherent, well-structured and accurate textual descriptions of
ontology classes.
Performance Analysis Of Regularized Linear Regression Models For Oxazolines And Oxazoles Derivitive Descriptor Dataset
Authors: Doreswamy, Chanabasayya . M. Vastrad
Date: 2013-12-10 00:00:00
Tasks: regression

Regularized regression techniques for linear regression have been created the
last few ten years to reduce the flaws of ordinary least squares regression
with regard to prediction accuracy. In this paper, new methods for using
regularized regression in model choice are introduced, and we distinguish the
conditions in which regularized regression develops our ability to discriminate
models. We applied all the five methods that use penalty-based (regularization)
shrinkage to handle Oxazolines and Oxazoles derivatives descriptor dataset with
far more predictors than observations. The lasso, ridge, elasticnet, lars and
relaxed lasso further possess the desirable property that they simultaneously
select relevant predictive descriptors and optimally estimate their effects.
Here, we comparatively evaluate the performance of five regularized linear
regression methods The assessment of the performance of each model by means of
benchmark experiments is an established exercise. Cross-validation and
resampling methods are generally used to arrive point evaluates the
efficiencies which are compared to recognize methods with acceptable features.
Predictive accuracy was evaluated using the root mean squared error (RMSE) and
Square of usual correlation between predictors and observed mean inhibitory
concentration of antitubercular activity (R square). We found that all five
regularized regression models were able to produce feasible models and
efficient capturing the linearity in the data. The elastic net and lars had
similar accuracies as well as lasso and relaxed lasso had similar accuracies
but outperformed ridge regression in terms of the RMSE and R square metrics.