pwc-archive/papers-with-abstracts huggingface dataset:
Beyond Bell's Theorem II: Scenarios with arbitrary causal structure
Authors: Tobias Fritz
Date: 2014-04-18 00:00:00
Tasks: Causal Inference

It has recently been found that Bell scenarios are only a small subclass of
interesting setups for studying the non-classical features of quantum theory
within spacetime. We find that it is possible to talk about classical
correlations, quantum correlations and other kinds of correlations on any
directed acyclic graph, and this captures various extensions of Bell scenarios
which have been considered in the literature. From a conceptual point of view,
the main feature of our approach is its high level of unification: while the
notions of source, choice of setting and measurement play all seemingly
different roles in a Bell scenario, our formalism shows that they are all
instances of the same concept of "event".
  Our work can also be understood as a contribution to the subject of causal
inference with latent variables. Among other things, we introduce hidden
Bayesian networks as a generalization of hidden Markov models.
Image Representations and New Domains in Neural Image Captioning
Authors: Jack Hessel, Nicolas Savva, Michael J. Wilber
Date: 2015-08-09 00:00:00
Proceeding: WS 2015 9
Tasks: Caption Generation, Image Captioning

We examine the possibility that recent promising results in automatic caption
generation are due primarily to language models. By varying image
representation quality produced by a convolutional neural network, we find that
a state-of-the-art neural captioning algorithm is able to produce quality
captions even when provided with surprisingly poor image representations. We
replicate this result in a new, fine-grained, transfer learned captioning
domain, consisting of 66K recipe image/title pairs. We also provide some
experiments regarding the appropriateness of datasets for automatic captioning,
and find that having multiple captions per image is beneficial, but not an
absolute requirement.
Sensitivity study using machine learning algorithms on simulated r-mode gravitational wave signals from newborn neutron stars
Authors: Antonis Mytidis, Athanasios Aris Panagopoulos, Orestis P. Panagopoulos, Andrew Miller, Bernard Whiting
Date: 2015-08-09 00:00:00
Tasks: BIG-bench Machine Learning, Sensitivity

This is a follow-up sensitivity study on r-mode gravitational wave signals
from newborn neutron stars illustrating the applicability of machine learning
algorithms for the detection of long-lived gravitational-wave transients. In
this sensitivity study we examine three machine learning algorithms (MLAs):
artificial neural networks (ANNs), support vector machines (SVMs) and
constrained subspace classifiers (CSCs). The objective of this study is to
compare the detection efficiency that MLAs can achieve with the efficiency of
conventional detection algorithms discussed in an earlier paper. Comparisons
are made using 2 distinct r-mode waveforms. For the training of the MLAs we
assumed that some information about the distance to the source is given so that
the training was performed over distance ranges not wider than half an order of
magnitude. The results of this study suggest that machine learning algorithms
are suitable for the detection of long-lived gravitational-wave transients and
that when assuming knowledge of the distance to the source, MLAs are at least
as efficient as conventional methods.
Crime Prediction Based On Crime Types And Using Spatial And Temporal Criminal Hotspots
Authors: Tahani Almanie, Rsha Mirza, Elizabeth Lor
Date: 2015-08-09 00:00:00
Tasks: Crime Prediction

This paper focuses on finding spatial and temporal criminal hotspots. It
analyses two different real-world crimes datasets for Denver, CO and Los
Angeles, CA and provides a comparison between the two datasets through a
statistical analysis supported by several graphs. Then, it clarifies how we
conducted Apriori algorithm to produce interesting frequent patterns for
criminal hotspots. In addition, the paper shows how we used Decision Tree
classifier and Naive Bayesian classifier in order to predict potential crime
types. To further analyse crimes datasets, the paper introduces an analysis
study by combining our findings of Denver crimes dataset with its demographics
information in order to capture the factors that might affect the safety of
neighborhoods. The results of this solution could be used to raise awareness
regarding the dangerous locations and to help agencies to predict future crimes
in a specific location within a particular time.
Security Games with Ambiguous Beliefs of Agents
Authors: Hossein Khani, Mohsen Afsharchi
Date: 2015-08-09 00:00:00

Currently the Dempster-Shafer based algorithm and Uniform Random Probability
based algorithm are the preferred method of resolving security games, in which
defenders are able to identify attackers and only strategy remained ambiguous.
However this model is inefficient in situations where resources are limited and
both the identity of the attackers and their strategies are ambiguous. The
intent of this study is to find a more effective algorithm to guide the
defenders in choosing which outside agents with which to cooperate given both
ambiguities. We designed an experiment where defenders were compelled to engage
with outside agents in order to maximize protection of their targets. We
introduced two important notions: the behavior of each agent in target
protection and the tolerance threshold in the target protection process. From
these, we proposed an algorithm that was applied by each defender to determine
the best potential assistant(s) with which to cooperate. Our results showed
that our proposed algorithm is safer than the Dempster-Shafer based algorithm.
Improving Decision Analytics with Deep Learning: The Case of Financial Disclosures
Authors: Stefan Feuerriegel, Ralph Fehrer
Date: 2015-08-09 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Decision analytics commonly focuses on the text mining of financial news
sources in order to provide managerial decision support and to predict stock
market movements. Existing predictive frameworks almost exclusively apply
traditional machine learning methods, whereas recent research indicates that
traditional machine learning methods are not sufficiently capable of extracting
suitable features and capturing the non-linear nature of complex tasks. As a
remedy, novel deep learning models aim to overcome this issue by extending
traditional neural network models with additional hidden layers. Indeed, deep
learning has been shown to outperform traditional methods in terms of
predictive performance. In this paper, we adapt the novel deep learning
technique to financial decision support. In this instance, we aim to predict
the direction of stock movements following financial disclosures. As a result,
we show how deep learning can outperform the accuracy of random forests as a
benchmark for machine learning by 5.66%.
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
Authors: Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael Mahoney
Date: 2014-12-29 00:00:00

We consider the problem of improving the efficiency of randomized Fourier
feature maps to accelerate training and testing speed of kernel methods on
large datasets. These approximate feature maps arise as Monte Carlo
approximations to integral representations of shift-invariant kernel functions
(e.g., Gaussian kernel). In this paper, we propose to use Quasi-Monte Carlo
(QMC) approximations instead, where the relevant integrands are evaluated on a
low-discrepancy sequence of points as opposed to random point sets as in the
Monte Carlo approach. We derive a new discrepancy measure called box
discrepancy based on theoretical characterizations of the integration error
with respect to a given sequence. We then propose to learn QMC sequences
adapted to our setting based on explicit box discrepancy minimization. Our
theoretical analyses are complemented with empirical results that demonstrate
the effectiveness of classical and adaptive QMC techniques for this problem.
Bidirectional LSTM-CRF Models for Sequence Tagging
Authors: Zhiheng Huang, Wei Xu, Kai Yu
Date: 2015-08-09 00:00:00
Tasks: Chunking, Named Entity Recognition (NER), POS, Sentence, TAG

In this paper, we propose a variety of Long Short-Term Memory (LSTM) based
models for sequence tagging. These models include LSTM networks, bidirectional
LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer
(LSTM-CRF) and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). Our work is
the first to apply a bidirectional LSTM CRF (denoted as BI-LSTM-CRF) model to
NLP benchmark sequence tagging data sets. We show that the BI-LSTM-CRF model
can efficiently use both past and future input features thanks to a
bidirectional LSTM component. It can also use sentence level tag information
thanks to a CRF layer. The BI-LSTM-CRF model can produce state of the art (or
close to) accuracy on POS, chunking and NER data sets. In addition, it is
robust and has less dependence on word embedding as compared to previous
observations.
Adaptive system optimization using random directions stochastic approximation
Authors: Prashanth L. A., Shalabh Bhatnagar, Michael Fu, Steve Marcus
Date: 2015-02-19 00:00:00

We present novel algorithms for simulation optimization using random
directions stochastic approximation (RDSA). These include first-order
(gradient) as well as second-order (Newton) schemes. We incorporate both
continuous-valued as well as discrete-valued perturbations into both our
algorithms. The former are chosen to be independent and identically distributed
(i.i.d.) symmetric, uniformly distributed random variables (r.v.), while the
latter are i.i.d., asymmetric, Bernoulli r.v.s. Our Newton algorithm, with a
novel Hessian estimation scheme, requires N-dimensional perturbations and three
loss measurements per iteration, whereas the simultaneous perturbation Newton
search algorithm of [1] requires 2N-dimensional perturbations and four loss
measurements per iteration. We prove the unbiasedness of both gradient and
Hessian estimates and asymptotic (strong) convergence for both first-order and
second-order schemes. We also provide asymptotic normality results, which in
particular establish that the asymmetric Bernoulli variant of Newton RDSA
method is better than 2SPSA of [1]. Numerical experiments are used to validate
the theoretical results.
Consistency of random forests
Authors: Erwan Scornet, Gérard Biau, Jean-Philippe Vert
Date: 2014-05-12 00:00:00
Tasks: Ensemble Learning, regression

Random forests are a learning algorithm proposed by Breiman [Mach. Learn. 45
(2001) 5--32] that combines several randomized decision trees and aggregates
their predictions by averaging. Despite its wide usage and outstanding
practical performance, little is known about the mathematical properties of the
procedure. This disparity between theory and practice originates in the
difficulty to simultaneously analyze both the randomization process and the
highly data-dependent tree structure. In the present paper, we take a step
forward in forest exploration by proving a consistency result for Breiman's
[Mach. Learn. 45 (2001) 5--32] original algorithm in the context of additive
regression models. Our analysis also sheds an interesting light on how random
forests can nicely adapt to sparsity. 1. Introduction. Random forests are an
ensemble learning method for classification and regression that constructs a
number of randomized decision trees during the training phase and predicts by
averaging the results. Since its publication in the seminal paper of Breiman
(2001), the procedure has become a major data analysis tool, that performs well
in practice in comparison with many standard methods. What has greatly
contributed to the popularity of forests is the fact that they can be applied
to a wide range of prediction problems and have few parameters to tune. Aside
from being simple to use, the method is generally recognized for its accuracy
and its ability to deal with small sample sizes, high-dimensional feature
spaces and complex data structures. The random forest methodology has been
successfully involved in many practical problems, including air quality
prediction (winning code of the EMC data science global hackathon in 2012, see
http://www.kaggle.com/c/dsg-hackathon), chemoinformatics [Svetnik et al.
(2003)], ecology [Prasad, Iverson and Liaw (2006), Cutler et al. (2007)], 3D
A variational approach to the consistency of spectral clustering
Authors: Nicolás García Trillos, Dejan Slepčev
Date: 2015-08-08 00:00:00
Tasks: Clustering

This paper establishes the consistency of spectral approaches to data
clustering. We consider clustering of point clouds obtained as samples of a
ground-truth measure. A graph representing the point cloud is obtained by
assigning weights to edges based on the distance between the points they
connect. We investigate the spectral convergence of both unnormalized and
normalized graph Laplacians towards the appropriate operators in the continuum
domain. We obtain sharp conditions on how the connectivity radius can be scaled
with respect to the number of sample points for the spectral convergence to
hold.
  We also show that the discrete clusters obtained via spectral clustering
converge towards a continuum partition of the ground truth measure. Such
continuum partition minimizes a functional describing the continuum analogue of
the graph-based spectral partitioning. Our approach, based on variational
convergence, is general and flexible.
A straightforward method to assess motion blur for different types of displays
Authors: Fuhao Chen, Jun Chen, Feng Huang
Date: 2015-08-08 00:00:00

A simulation method based on the liquid crystal response and the human visual
system is suitable to characterize motion blur for LCDs but not other display
types. We propose a more straightforward and widely applicable method to
quantify motion blur based on the width of the moving object. We thus compare
various types of displays objectively. A perceptual experiment was conducted to
validate the proposed method. We test varying motion velocities for nine
commercial displays. We compare the three motion blur evaluation methods
(simulation, human perception, and our method) using z-scores. Our comparisons
indicate that our method accurately characterizes motion blur for various
display types.
Input anticipating critical reservoirs show power law forgetting of unexpected input events
Authors: Norbert Michael Mayer
Date: 2014-04-25 00:00:00

Usually, reservoir computing shows an exponential memory decay. This paper
investigates under which circumstances echo state networks can show a power law
forgetting. That means traces of earlier events can be found in the reservoir
for very long time spans. Such a setting requires critical connectivity exactly
at the limit of what is permissible according the echo state condition.
However, for general matrices the limit cannot be determined exactly from
theory. In addition, the behavior of the network is strongly influenced by the
input flow. Results are presented that use certain types of restricted
recurrent connectivity and anticipation learning with regard to the input,
where indeed power law forgetting can be achieved.
Extensions of stability selection using subsamples of observations and covariates
Authors: Andre Beinrucker, Ürün Dogan, Gilles Blanchard
Date: 2014-07-18 00:00:00
Tasks: Variable Selection

We introduce extensions of stability selection, a method to stabilise
variable selection methods introduced by Meinshausen and B\"uhlmann (J R Stat
Soc 72:417-473, 2010). We propose to apply a base selection method repeatedly
to random observation subsamples and covariate subsets under scrutiny, and to
select covariates based on their selection frequency. We analyse the effects
and benefits of these extensions. Our analysis generalizes the theoretical
results of Meinshausen and B\"uhlmann (J R Stat Soc 72:417-473, 2010) from the
case of half-samples to subsamples of arbitrary size. We study, in a
theoretical manner, the effect of taking random covariate subsets using a
simplified score model. Finally we validate these extensions on numerical
experiments on both synthetic and real datasets, and compare the obtained
results in detail to the original stability selection method.
Simulation of optical flow and fuzzy based obstacle avoidance system for mobile robots
Authors: G. D. Illeperuma, D. U. J. Sonnadara
Date: 2015-08-08 00:00:00
Tasks: Navigate, Optical Flow Estimation

Honey bees use optical flow to avoid obstacles effectively. In this research
work similar methodology was tested on a simulated mobile robot. Simulation
framework was based on VRML and Simulink in a 3D world. Optical flow vectors
were calculated from a video scene captured by a virtual camera which was used
as inputs to a fuzzy logic controller. Fuzzy logic controller decided the
locomotion of the robot. Different fuzzy logic rules were evaluated. The robot
was able to navigate through complex static and dynamic environments
effectively, avoiding obstacles on its path.
Decomposition and Identification of Linear Structural Equation Models
Authors: Bryant Chen
Date: 2015-08-07 00:00:00

In this paper, we address the problem of identifying linear structural
equation models. We first extend the edge set half-trek criterion to cover a
broader class of models. We then show that any semi-Markovian linear model can
be recursively decomposed into simpler sub-models, resulting in improved
identification power. Finally, we show that, unlike the existing methods
developed for linear models, the resulting method subsumes the identification
algorithm of non-parametric models.
Spectral Clustering and Block Models: A Review And A New Algorithm
Authors: Sharmodeep Bhattacharyya, Peter J. Bickel
Date: 2015-08-07 00:00:00
Tasks: Clustering, Image Clustering

We focus on spectral clustering of unlabeled graphs and review some results
on clustering methods which achieve weak or strong consistent identification in
data generated by such models. We also present a new algorithm which appears to
perform optimally both theoretically using asymptotic theory and empirically.
Mimicry Is Presidential: Linguistic Style Matching in Presidential Debates and Improved Polling Numbers
Authors: Daniel M. Romero, Roderick I. Swaab, Brian Uzzi, Adam D. Galinsky
Date: 2015-08-07 00:00:00

The current research used the contexts of U.S. presidential debates and
negotiations to examine whether matching the linguistic style of an opponent in
a two-party exchange affects the reactions of third-party observers. Building
off communication accommodation theory (CAT), interaction alignment theory
(IAT), and processing fluency, we propose that language style matching (LSM)
will improve subsequent third-party evaluations because matching an opponent's
linguistic style reflects greater perspective taking and will make one's
arguments easier to process. In contrast, research on status inferences
predicts that LSM will negatively impact third-party evaluations because LSM
implies followership. We conduct two studies to test these competing
hypotheses. Study 1 analyzed transcripts of U.S. presidential debates between
1976 and 2012 and found that candidates who matched their opponent's linguistic
style increased their standing in the polls. Study 2 demonstrated a causal
relationship between LSM and third-party observer evaluations using negotiation
transcripts.
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
Authors: Tsung-Hsien Wen, Milica Gasic, Dongho Kim, Nikola Mrksic, Pei-Hao Su, David Vandyke, Steve Young
Date: 2015-08-07 00:00:00
Proceeding: WS 2015 9
Tasks: Reranking, Sentence, Text Generation

The natural language generation (NLG) component of a spoken dialogue system
(SDS) usually needs a substantial amount of handcrafting or a well-labeled
dataset to be trained on. These limitations add significantly to development
costs and make cross-domain, multi-lingual dialogue systems intractable.
Moreover, human languages are context-aware. The most natural response should
be directly learned from data rather than depending on predefined syntaxes or
rules. This paper presents a statistical language generator based on a joint
recurrent and convolutional neural network structure which can be trained on
dialogue act-utterance pairs without any semantic alignments or predefined
grammar trees. Objective metrics suggest that this new model outperforms
previous methods under the same experimental conditions. Results of an
evaluation by human judges indicate that it produces not only high quality but
linguistically varied utterances which are preferred compared to n-gram and
rule-based systems.
Agglomerative clustering and collectiveness measure via exponent generating function
Authors: Wei-Ya Ren, Shuo-Hao Li, Qiang Guo, Guo-Hui Li, Jun Zhang
Date: 2015-07-30 00:00:00
Tasks: Clustering

The key in agglomerative clustering is to define the affinity measure between
two sets. A novel agglomerative clustering method is proposed by utilizing the
path integral to define the affinity measure. Firstly, the path integral
descriptor of an edge, a node and a set is computed by path integral and
exponent generating function. Then, the affinity measure between two sets is
obtained by path integral descriptor of sets. Several good properties of the
path integral descriptor is proposed in this paper. In addition, we give the
physical interpretation of the proposed path integral descriptor of a set. The
proposed path integral descriptor of a set can be regard as the collectiveness
measure of a set, which can be a moving system such as human crowd, sheep herd
and so on. Self-driven particle (SDP) model is used to test the ability of the
proposed method in measuring collectiveness.
Study of Phonemes Confusions in Hierarchical Automatic Phoneme Recognition System
Authors: Rimah Amami, Noureddine Ellouze
Date: 2015-08-07 00:00:00
Tasks: Automatic Phoneme Recognition, Phoneme Recognition

In this paper, we have analyzed the impact of confusions on the robustness of
phoneme recognitions system. The confusions are detected at the pronunciation
and the confusions matrices of the phoneme recognizer. The confusions show that
some similarities between phonemes at the pronunciation affect significantly
the recognition rates. This paper proposes to understand those confusions in
order to improve the performance of the phoneme recognition system by isolating
the problematic phonemes. Confusion analysis leads to build a new hierarchical
recognizer using new phoneme distribution and the information from the
confusion matrices. This new hierarchical phoneme recognition system shows
significant improvements of the recognition rates on TIMIT database.
Dimension reduction for model-based clustering
Authors: Luca Scrucca
Date: 2015-08-07 00:00:00
Tasks: Clustering, Dimensionality Reduction, model

We introduce a dimension reduction method for visualizing the clustering
structure obtained from a finite mixture of Gaussian densities. Information on
the dimension reduction subspace is obtained from the variation on group means
and, depending on the estimated mixture model, on the variation on group
covariances. The proposed method aims at reducing the dimensionality by
identifying a set of linear combinations, ordered by importance as quantified
by the associated eigenvalues, of the original features which capture most of
the cluster structure contained in the data. Observations may then be projected
onto such a reduced subspace, thus providing summary plots which help to
visualize the clustering structure. These plots can be particularly appealing
in the case of high-dimensional data and noisy structure. The new constructed
variables capture most of the clustering information available in the data, and
they can be further reduced to improve clustering performance. We illustrate
the approach on both simulated and real data sets.