pwc-archive/papers-with-abstracts huggingface dataset:
Unveiling the Political Agenda of the European Parliament Plenary: A Topical Analysis
Authors: Derek Greene, James P. Cross
Date: 2015-05-27 00:00:00
Tasks: Dynamic Topic Modeling

This study analyzes political interactions in the European Parliament (EP) by
considering how the political agenda of the plenary sessions has evolved over
time and the manner in which Members of the European Parliament (MEPs) have
reacted to external and internal stimuli when making Parliamentary speeches. It
does so by considering the context in which speeches are made, and the content
of those speeches. To detect latent themes in legislative speeches over time,
speech content is analyzed using a new dynamic topic modeling method, based on
two layers of matrix factorization. This method is applied to a new corpus of
all English language legislative speeches in the EP plenary from the period
1999-2014. Our findings suggest that the political agenda of the EP has evolved
significantly over time, is impacted upon by the committee structure of the
Parliament, and reacts to exogenous events such as EU Treaty referenda and the
emergence of the Euro-crisis have a significant impact on what is being
discussed in Parliament.
Prediction of Radiation Fog by DNA Computing
Authors: Kumar Sankar Ray, Mandrita Mondal
Date: 2015-07-07 00:00:00
Tasks: Prediction

In this paper we propose a wet lab algorithm for prediction of radiation fog
by DNA computing. The concept of DNA computing is essentially exploited for
generating the classifier algorithm in the wet lab. The classifier is based on
a new concept of similarity based fuzzy reasoning suitable for wet lab
implementation. This new concept of similarity based fuzzy reasoning is
different from conventional approach to fuzzy reasoning based on similarity
measure and also replaces the logical aspect of classical fuzzy reasoning by
DNA chemistry. Thus, we add a new dimension to existing forms of fuzzy
reasoning by bringing it down to nanoscale. We exploit the concept of massive
parallelism of DNA computing by designing this new classifier in the wet lab.
This newly designed classifier is very much generalized in nature and apart
from prediction of radiation fog this methodology can be applied to other types
of data also. To achieve our goal we first fuzzify the given observed
parameters in a form of synthetic DNA sequence which is called fuzzy DNA and
which handles the vague concept of human reasoning.
A Survey and Classification of Controlled Natural Languages
Authors: Tobias Kuhn
Date: 2015-07-07 00:00:00
Proceeding: CL 2014 3
Tasks: Classification, General Classification, Survey, Translation

What is here called controlled natural language (CNL) has traditionally been
given many different names. Especially during the last four decades, a wide
variety of such languages have been designed. They are applied to improve
communication among humans, to improve translation, or to provide natural and
intuitive representations for formal notations. Despite the apparent
differences, it seems sensible to put all these languages under the same
umbrella. To bring order to the variety of languages, a general classification
scheme is presented here. A comprehensive survey of existing English-based CNLs
is given, listing and describing 100 languages from 1930 until today.
Classification of these languages reveals that they form a single scattered
cloud filling the conceptual space between natural languages such as English on
the one end and formal languages such as propositional logic on the other. The
goal of this article is to provide a common terminology and a common model for
CNL, to contribute to the understanding of their general nature, to provide a
starting point for researchers interested in the area, and to help developers
to make design decisions.
Learning Tractable Probabilistic Models for Fault Localization
Authors: Aniruddh Nath, Pedro Domingos
Date: 2015-07-07 00:00:00
Tasks: Fault localization

In recent years, several probabilistic techniques have been applied to
various debugging problems. However, most existing probabilistic debugging
systems use relatively simple statistical models, and fail to generalize across
multiple programs. In this work, we propose Tractable Fault Localization Models
(TFLMs) that can be learned from data, and probabilistically infer the location
of the bug. While most previous statistical debugging methods generalize over
many executions of a single program, TFLMs are trained on a corpus of
previously seen buggy programs, and learn to identify recurring patterns of
bugs. Widely-used fault localization techniques such as TARANTULA evaluate the
suspiciousness of each line in isolation; in contrast, a TFLM defines a joint
probability distribution over buggy indicator variables for each line. Joint
distributions with rich dependency structure are often computationally
intractable; TFLMs avoid this by exploiting recent developments in tractable
probabilistic models (specifically, Relational SPNs). Further, TFLMs can
incorporate additional sources of information, including coverage-based
features such as TARANTULA. We evaluate the fault localization performance of
TFLMs that include TARANTULA scores as features in the probabilistic model. Our
study shows that the learned TFLMs isolate bugs more effectively than previous
statistical methods or using TARANTULA directly.
Developing Postfix-GP Framework for Symbolic Regression Problems
Authors: Vipul K. Dabhi, Sanjay Chaudhary
Date: 2015-07-07 00:00:00
Tasks: regression, Symbolic Regression

This paper describes Postfix-GP system, postfix notation based Genetic
Programming (GP), for solving symbolic regression problems. It presents an
object-oriented architecture of Postfix-GP framework. It assists the user in
understanding of the implementation details of various components of
Postfix-GP. Postfix-GP provides graphical user interface which allows user to
configure the experiment, to visualize evolved solutions, to analyze GP run,
and to perform out-of-sample predictions. The use of Postfix-GP is demonstrated
by solving the benchmark symbolic regression problem. Finally, features of
Postfix-GP framework are compared with that of other GP systems.
Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches
Authors: Xiao Fu, Wing-Kin Ma, José Bioucas-Dias, Tsung-Han Chan
Date: 2015-07-07 00:00:00
Tasks: Hyperspectral Unmixing, regression

The dictionary-aided sparse regression (SR) approach has recently emerged as
a promising alternative to hyperspectral unmixing (HU) in remote sensing. By
using an available spectral library as a dictionary, the SR approach identifies
the underlying materials in a given hyperspectral image by selecting a small
subset of spectral samples in the dictionary to represent the whole image. A
drawback with the current SR developments is that an actual spectral signature
in the scene is often assumed to have zero mismatch with its corresponding
dictionary sample, and such an assumption is considered too ideal in practice.
In this paper, we tackle the spectral signature mismatch problem by proposing a
dictionary-adjusted nonconvex sparsity-encouraging regression (DANSER)
framework. The main idea is to incorporate dictionary correcting variables in
an SR formulation. A simple and low per-iteration complexity algorithm is
tailor-designed for practical realization of DANSER. Using the same dictionary
correcting idea, we also propose a robust subspace solution for dictionary
pruning. Extensive simulations and real-data experiments show that the proposed
method is effective in mitigating the undesirable spectral signature mismatch
effects.
Jointly Embedding Relations and Mentions for Knowledge Population
Authors: Miao Fan, Kai Cao, Yifan He, Ralph Grishman
Date: 2015-04-07 00:00:00
Proceeding: RANLP 2015 9
Tasks: Relation, Relation Extraction

This paper contributes a joint embedding model for predicting relations
between a pair of entities in the scenario of relation inference. It differs
from most stand-alone approaches which separately operate on either knowledge
bases or free texts. The proposed model simultaneously learns low-dimensional
vector representations for both triplets in knowledge repositories and the
mentions of relations in free texts, so that we can leverage the evidence both
resources to make more accurate predictions. We use NELL to evaluate the
performance of our approach, compared with cutting-edge methods. Results of
extensive experiments show that our model achieves significant improvement on
relation extraction.
A model of sensory neural responses in the presence of unknown modulatory inputs
Authors: Neil C. Rabinowitz, Robbe L. T. Goris, Johannes Ballé, Eero P. Simoncelli
Date: 2015-07-06 00:00:00

Neural responses are highly variable, and some portion of this variability
arises from fluctuations in modulatory factors that alter their gain, such as
adaptation, attention, arousal, expected or actual reward, emotion, and local
metabolic resource availability. Regardless of their origin, fluctuations in
these signals can confound or bias the inferences that one derives from spiking
responses. Recent work demonstrates that for sensory neurons, these effects can
be captured by a modulated Poisson model, whose rate is the product of a
stimulus-driven response function and an unknown modulatory signal. Here, we
extend this model, by incorporating explicit modulatory elements that are known
(specifically, spike-history dependence, as in previous models), and by
constraining the remaining latent modulatory signals to be smooth in time. We
develop inference procedures for fitting the entire model, including
hyperparameters, via evidence optimization, and apply these to simulated data,
and to responses of ferret auditory midbrain and cortical neurons to complex
sounds. We show that integrating out the latent modulators yields better (or
more readily-interpretable) receptive field estimates than a standard Poisson
model. Conversely, integrating out the stimulus dependence yields estimates of
the slowly-varying latent modulators.
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Noisy Matrix Decomposition
Authors: Hanie Sedghi, Anima Anandkumar, Edmond Jonckheere
Date: 2014-02-20 00:00:00
Proceeding: NeurIPS 2014

We propose an efficient ADMM method with guarantees for high-dimensional
problems. We provide explicit bounds for the sparse optimization problem and
the noisy matrix decomposition problem. For sparse optimization, we establish
that the modified ADMM method has an optimal convergence rate of
$\mathcal{O}(s\log d/T)$, where $s$ is the sparsity level, $d$ is the data
dimension and $T$ is the number of steps. This matches with the minimax lower
bounds for sparse estimation. For matrix decomposition into sparse and low rank
components, we provide the first guarantees for any online method, and prove a
convergence rate of $\tilde{\mathcal{O}}((s+r)\beta^2(p) /T) +
\mathcal{O}(1/p)$ for a $p\times p$ matrix, where $s$ is the sparsity level,
$r$ is the rank and $\Theta(\sqrt{p})\leq \beta(p)\leq \Theta(p)$. Our
guarantees match the minimax lower bound with respect to $s,r$ and $T$. In
addition, we match the minimax lower bound with respect to the matrix dimension
$p$, i.e. $\beta(p)=\Theta(\sqrt{p})$, for many important statistical models
including the independent noise model, the linear Bayesian network and the
latent Gaussian graphical model under some conditions. Our ADMM method is based
on epoch-based annealing and consists of inexpensive steps which involve
projections on to simple norm balls. Experiments show that for both sparse
optimization and matrix decomposition problems, our algorithm outperforms the
state-of-the-art methods. In particular, we reach higher accuracy with same
time complexity.
Reflections on Sentiment/Opinion Analysis
Authors: Jiwei Li, Eduard Hovy
Date: 2015-07-06 00:00:00

In this paper, we described possible directions for deeper understanding,
helping bridge the gap between psychology / cognitive science and computational
approaches in sentiment/opinion analysis literature. We focus on the opinion
holder's underlying needs and their resultant goals, which, in a utilitarian
model of sentiment, provides the basis for explaining the reason a sentiment
valence is held. While these thoughts are still immature, scattered,
unstructured, and even imaginary, we believe that these perspectives might
suggest fruitful avenues for various kinds of future work.
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation
Authors: Huan Gui, Quanquan Gu
Date: 2015-05-18 00:00:00
Tasks: Matrix Completion

We present a unified framework for low-rank matrix estimation with nonconvex
penalties. We first prove that the proposed estimator attains a faster
statistical rate than the traditional low-rank matrix estimator with nuclear
norm penalty. Moreover, we rigorously show that under a certain condition on
the magnitude of the nonzero singular values, the proposed estimator enjoys
oracle property (i.e., exactly recovers the true rank of the matrix), besides
attaining a faster rate. As far as we know, this is the first work that
establishes the theory of low-rank matrix estimation with nonconvex penalties,
confirming the advantages of nonconvex penalties for matrix completion.
Numerical experiments on both synthetic and real world datasets corroborate our
theory.
Emphatic Temporal-Difference Learning
Authors: A. Rupam Mahmood, Huizhen Yu, Martha White, Richard S. Sutton
Date: 2015-07-06 00:00:00

Emphatic algorithms are temporal-difference learning algorithms that change
their effective state distribution by selectively emphasizing and
de-emphasizing their updates on different time steps. Recent works by Sutton,
Mahmood and White (2015), and Yu (2015) show that by varying the emphasis in a
particular way, these algorithms become stable and convergent under off-policy
training with linear function approximation. This paper serves as a unified
summary of the available results from both works. In addition, we demonstrate
the empirical benefits from the flexibility of emphatic algorithms, including
state-dependent discounting, state-dependent bootstrapping, and the
user-specified allocation of function approximation resources.
A Simple Algorithm for Maximum Margin Classification, Revisited
Authors: Sariel Har-Peled
Date: 2015-07-06 00:00:00
Tasks: Classification, General Classification

In this note, we revisit the algorithm of Har-Peled et. al. [HRZ07] for
computing a linear maximum margin classifier. Our presentation is self
contained, and the algorithm itself is slightly simpler than the original
algorithm. The algorithm itself is a simple Perceptron like iterative
algorithm. For more details and background, the reader is referred to the
original paper.
Correspondence Factor Analysis of Big Data Sets: A Case Study of 30 Million Words; and Contrasting Analytics using Apache Solr and Correspondence Analysis in R
Authors: Fionn Murtagh
Date: 2015-07-06 00:00:00

We consider a large number of text data sets. These are cooking recipes. Term
distribution and other distributional properties of the data are investigated.
Our aim is to look at various analytical approaches which allow for mining of
information on both high and low detail scales. Metric space embedding is
fundamental to our interest in the semantic properties of this data. We
consider the projection of all data into analyses of aggregated versions of the
data. We contrast that with projection of aggregated versions of the data into
analyses of all the data. Analogously for the term set, we look at analysis of
selected terms. We also look at inherent term associations such as between
singular and plural. In addition to our use of Correspondence Analysis in R,
for latent semantic space mapping, we also use Apache Solr. Setting up the Solr
server and carrying out querying is described. A further novelty is that
querying is supported in Solr based on the principal factor plane mapping of
all the data. This uses a bounding box query, based on factor projections.
Sparse Approximate Inference for Spatio-Temporal Point Process Models
Authors: Botond Cseke, Andrew Zammit Mangion, Tom Heskes, Guido Sanguinetti
Date: 2013-05-17 00:00:00

Spatio-temporal point process models play a central role in the analysis of
spatially distributed systems in several disciplines. Yet, scalable inference
remains computa- tionally challenging both due to the high resolution modelling
generally required and the analytically intractable likelihood function. Here,
we exploit the sparsity structure typical of (spatially) discretised
log-Gaussian Cox process models by using approximate message-passing
algorithms. The proposed algorithms scale well with the state dimension and the
length of the temporal horizon with moderate loss in distributional accuracy.
They hence provide a flexible and faster alternative to both non-linear
filtering-smoothing type algorithms and to approaches that implement the
Laplace method or expectation propagation on (block) sparse latent Gaussian
models. We infer the parameters of the latent Gaussian model using a structured
variational Bayes approach. We demonstrate the proposed framework on simulation
studies with both Gaussian and point-process observations and use it to
reconstruct the conflict intensity and dynamics in Afghanistan from the
WikiLeaks Afghan War Diary.
Semi-proximal Mirror-Prox for Nonsmooth Composite Minimization
Authors: Niao He, Zaid Harchaoui
Date: 2015-07-06 00:00:00
Proceeding: NeurIPS 2015 12

We propose a new first-order optimisation algorithm to solve high-dimensional
non-smooth composite minimisation problems. Typical examples of such problems
have an objective that decomposes into a non-smooth empirical risk part and a
non-smooth regularisation penalty. The proposed algorithm, called Semi-Proximal
Mirror-Prox, leverages the Fenchel-type representation of one part of the
objective while handling the other part of the objective via linear
minimization over the domain. The algorithm stands in contrast with more
classical proximal gradient algorithms with smoothing, which require the
computation of proximal operators at each iteration and can therefore be
impractical for high-dimensional problems. We establish the theoretical
convergence rate of Semi-Proximal Mirror-Prox, which exhibits the optimal
complexity bounds, i.e. $O(1/\epsilon^2)$, for the number of calls to linear
minimization oracle. We present promising experimental results showing the
interest of the approach in comparison to competing methods.
Revisiting Large Scale Distributed Machine Learning
Authors: Radu Cristian Ionescu
Date: 2015-07-06 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Nowadays, with the widespread of smartphones and other portable gadgets
equipped with a variety of sensors, data is ubiquitous available and the focus
of machine learning has shifted from being able to infer from small training
samples to dealing with large scale high-dimensional data. In domains such as
personal healthcare applications, which motivates this survey, distributed
machine learning is a promising line of research, both for scaling up learning
algorithms, but mostly for dealing with data which is inherently produced at
different locations. This report offers a thorough overview of and
state-of-the-art algorithms for distributed machine learning, for both
supervised and unsupervised learning, ranging from simple linear logistic
regression to graphical models and clustering. We propose future directions for
most categories, specific to the potential personal healthcare applications.
With this in mind, the report focuses on how security and low communication
overhead can be assured in the specific case of a strictly client-server
architectural model. As particular directions we provides an exhaustive
presentation of an empirical clustering algorithm, k-windows, and proposed an
asynchronous distributed machine learning algorithm that would scale well and
also would be computationally cheap and easy to implement.
Towards Data-Driven Autonomics in Data Centers
Authors: Alina Sîrbu, Ozalp Babaoglu
Date: 2015-05-19 00:00:00
Tasks: Management

Continued reliance on human operators for managing data centers is a major
impediment for them from ever reaching extreme dimensions. Large computer
systems in general, and data centers in particular, will ultimately be managed
using predictive computational and executable models obtained through
data-science tools, and at that point, the intervention of humans will be
limited to setting high-level goals and policies rather than performing
low-level operations. Data-driven autonomics, where management and control are
based on holistic predictive models that are built and updated using generated
data, opens one possible path towards limiting the role of operators in data
centers. In this paper, we present a data-science study of a public Google
dataset collected in a 12K-node cluster with the goal of building and
evaluating a predictive model for node failures. We use BigQuery, the big data
SQL platform from the Google Cloud suite, to process massive amounts of data
and generate a rich feature set characterizing machine state over time. We
describe how an ensemble classifier can be built out of many Random Forest
classifiers each trained on these features, to predict if machines will fail in
a future 24-hour window. Our evaluation reveals that if we limit false positive
rates to 5%, we can achieve true positive rates between 27% and 88% with
precision varying between 50% and 72%. We discuss the practicality of including
our predictive model as the central component of a data-driven autonomic
manager and operating it on-line with live data streams (rather than off-line
on data logs). All of the scripts used for BigQuery and classification analyses
are publicly available from the authors' website.
Learning Better Encoding for Approximate Nearest Neighbor Search with Dictionary Annealing
Authors: Shicong Liu, Hongtao Lu
Date: 2015-07-06 00:00:00
Tasks: Dictionary Learning, GPU, Quantization

We introduce a novel dictionary optimization method for high-dimensional
vector quantization employed in approximate nearest neighbor (ANN) search.
Vector quantization methods first seek a series of dictionaries, then
approximate each vector by a sum of elements selected from these dictionaries.
An optimal series of dictionaries should be mutually independent, and each
dictionary should generate a balanced encoding for the target dataset. Existing
methods did not explicitly consider this. To achieve these goals along with
minimizing the quantization error (residue), we propose a novel dictionary
optimization method called \emph{Dictionary Annealing} that alternatively
"heats up" a single dictionary by generating an intermediate dataset with
residual vectors, "cools down" the dictionary by fitting the intermediate
dataset, then extracts the new residual vectors for the next iteration. Better
codes can be learned by DA for the ANN search tasks. DA is easily implemented
on GPU to utilize the latest computing technology, and can easily extended to
an online dictionary learning scheme. We show by experiments that our optimized
dictionaries substantially reduce the overall quantization error. Jointly used
with residual vector quantization, our optimized dictionaries lead to a better
approximate nearest neighbor search performance compared to the
state-of-the-art methods.