pwc-archive/papers-with-abstracts huggingface dataset:
Linguistics and some aspects of its underlying dynamics
Authors: Massimo Piattelli-Palmarini, Giuseppe Vitiello
Date: 2015-06-14 00:00:00
Tasks: Sentence

In recent years, central components of a new approach to linguistics, the
Minimalist Program (MP) have come closer to physics. Features of the Minimalist
Program, such as the unconstrained nature of recursive Merge, the operation of
the Labeling Algorithm that only operates at the interface of Narrow Syntax
with the Conceptual-Intentional and the Sensory-Motor interfaces, the
difference between pronounced and un-pronounced copies of elements in a
sentence and the build-up of the Fibonacci sequence in the syntactic derivation
of sentence structures, are directly accessible to representation in terms of
algebraic formalism. Although in our scheme linguistic structures are classical
ones, we find that an interesting and productive isomorphism can be established
between the MP structure, algebraic structures and many-body field theory
opening new avenues of inquiry on the dynamics underlying some central aspects
of linguistics.
Artificial general intelligence through recursive data compression and grounded reasoning: a position paper
Authors: Arthur Franz
Date: 2015-06-14 00:00:00
Tasks: Data Compression, Inductive Bias, Position

This paper presents a tentative outline for the construction of an
artificial, generally intelligent system (AGI). It is argued that building a
general data compression algorithm solving all problems up to a complexity
threshold should be the main thrust of research. A measure for partial progress
in AGI is suggested. Although the details are far from being clear, some
general properties for a general compression algorithm are fleshed out. Its
inductive bias should be flexible and adapt to the input data while constantly
searching for a simple, orthogonal and complete set of hypotheses explaining
the data. It should recursively reduce the size of its representations thereby
compressing the data increasingly at every iteration.
  Abstract Based on that fundamental ability, a grounded reasoning system is
proposed. It is argued how grounding and flexible feature bases made of
hypotheses allow for resourceful thinking. While the simulation of
representation contents on the mental stage accounts for much of the power of
propositional logic, compression leads to simple sets of hypotheses that allow
the detection and verification of universally quantified statements.
  Abstract Together, it is highlighted how general compression and grounded
reasoning could account for the birth and growth of first concepts about the
world and the commonsense reasoning about them.
Leveraging Word Embeddings for Spoken Document Summarization
Authors: Kuan-Yu Chen, Shih-Hung Liu, Hsin-Min Wang, Berlin Chen, Hsin-Hsi Chen
Date: 2015-06-14 00:00:00
Tasks: Document Summarization, Sentence, Word Embeddings

Owing to the rapidly growing multimedia content available on the Internet,
extractive spoken document summarization, with the purpose of automatically
selecting a set of representative sentences from a spoken document to concisely
express the most important theme of the document, has been an active area of
research and experimentation. On the other hand, word embedding has emerged as
a newly favorite research subject because of its excellent performance in many
natural language processing (NLP)-related tasks. However, as far as we are
aware, there are relatively few studies investigating its use in extractive
text or speech summarization. A common thread of leveraging word embeddings in
the summarization process is to represent the document (or sentence) by
averaging the word embeddings of the words occurring in the document (or
sentence). Then, intuitively, the cosine similarity measure can be employed to
determine the relevance degree between a pair of representations. Beyond the
continued efforts made to improve the representation of words, this paper
focuses on building novel and efficient ranking models based on the general
word embedding methods for extractive speech summarization. Experimental
results demonstrate the effectiveness of our proposed methods, compared to
existing state-of-the-art methods.
Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph
Authors: Fei Tian, Bin Gao, Enhong Chen, Tie-Yan Liu
Date: 2015-05-19 00:00:00
Tasks: Knowledge Graphs

Word embedding, which refers to low-dimensional dense vector representations
of natural words, has demonstrated its power in many natural language
processing tasks. However, it may suffer from the inaccurate and incomplete
information contained in the free text corpus as training data. To tackle this
challenge, there have been quite a few works that leverage knowledge graphs as
an additional information source to improve the quality of word embedding.
Although these works have achieved certain success, they have neglected some
important facts about knowledge graphs: (i) many relationships in knowledge
graphs are \emph{many-to-one}, \emph{one-to-many} or even \emph{many-to-many},
rather than simply \emph{one-to-one}; (ii) most head entities and tail entities
in knowledge graphs come from very different semantic spaces. To address these
issues, in this paper, we propose a new algorithm named ProjectNet. ProjecNet
models the relationships between head and tail entities after transforming them
with different low-rank projection matrices. The low-rank projection can allow
non \emph{one-to-one} relationships between entities, while different
projection matrices for head and tail entities allow them to originate in
different semantic spaces. The experimental results demonstrate that ProjectNet
yields more accurate word embedding than previous works, thus leads to clear
improvements in various natural language processing tasks.
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms
Authors: Yunwen Lei, Ürün Dogan, Alexander Binder, Marius Kloft
Date: 2015-06-14 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: General Classification, Generalization Bounds, Multi-class Classification

This paper studies the generalization performance of multi-class
classification algorithms, for which we obtain, for the first time, a
data-dependent generalization error bound with a logarithmic dependence on the
class size, substantially improving the state-of-the-art linear dependence in
the existing data-dependent generalization analysis. The theoretical analysis
motivates us to introduce a new multi-class classification machine based on
$\ell_p$-norm regularization, where the parameter $p$ controls the complexity
of the corresponding bounds. We derive an efficient optimization algorithm
based on Fenchel duality theory. Benchmarks on several real-world datasets show
that the proposed algorithm can achieve significant accuracy gains over the
state of the art.
The Artists who Forged Themselves: Detecting Creativity in Art
Authors: Milan Rajković, Miloš Milovanović
Date: 2015-06-14 00:00:00

Creativity and the understanding of cognitive processes involved in the
creative process are relevant to all of human activities. Comprehension of
creativity in the arts is of special interest due to the involvement of many
scientific and non scientific disciplines. Using digital representation of
paintings, we show that creative process in painting art may be objectively
recognized within the mathematical framework of self organization, a process
characteristic of nonlinear dynamic systems and occurring in natural and social
sciences. Unlike the artist identification process or the recognition of
forgery, which presupposes the knowledge of the original work, our method
requires no prior knowledge on the originality of the work of art. The original
paintings are recognized as realizations of the creative process which, in
general, is shown to correspond to self-organization of texture features which
determine the aesthetic complexity of the painting. The method consists of the
wavelet based statistical digital image processing and the measure of
statistical complexity which represents the minimal (average) information
necessary for optimal prediction. The statistical complexity is based on the
properly defined causal states with optimal predictive properties. Two
different time concepts related to the works of art are introduced: the
internal time and the artistic time. The internal time of the artwork is
determined by the span of causal dependencies between wavelet coefficients
while the artistic time refers to the internal time during which complexity
increases where complexity refers to compositional, aesthetic and structural
arrangement of texture features. The method is illustrated by recognizing the
original paintings from the copies made by the artists themselves, including
the works of the famous surrealist painter Ren\'{e} Magritte.
Rare Speed-up in Automatic Theorem Proving Reveals Tradeoff Between Computational Time and Information Value
Authors: Santiago Hernández-Orozco, Francisco Hernández-Quiroz, Hector Zenil, Wilfried Sieg
Date: 2015-06-14 00:00:00
Tasks: Automated Theorem Proving

We show that strategies implemented in automatic theorem proving involve an
interesting tradeoff between execution speed, proving speedup/computational
time and usefulness of information. We advance formal definitions for these
concepts by way of a notion of normality related to an expected (optimal)
theoretical speedup when adding useful information (other theorems as axioms),
as compared with actual strategies that can be effectively and efficiently
implemented. We propose the existence of an ineluctable tradeoff between this
normality and computational time complexity. The argument quantifies the
usefulness of information in terms of (positive) speed-up. The results disclose
a kind of no-free-lunch scenario and a tradeoff of a fundamental nature. The
main theorem in this paper together with the numerical experiment---undertaken
using two different automatic theorem provers AProS and Prover9 on random
theorems of propositional logic---provide strong theoretical and empirical
arguments for the fact that finding new useful information for solving a
specific problem (theorem) is, in general, as hard as the problem (theorem)
itself.
Deep Secure Encoding: An Application to Face Recognition
Authors: Rohit Pandey, Yingbo Zhou, Venu Govindaraju
Date: 2015-06-14 00:00:00
Tasks: Face Recognition, General Classification

In this paper we present Deep Secure Encoding: a framework for secure
classification using deep neural networks, and apply it to the task of
biometric template protection for faces. Using deep convolutional neural
networks (CNNs), we learn a robust mapping of face classes to high entropy
secure codes. These secure codes are then hashed using standard hash functions
like SHA-256 to generate secure face templates. The efficacy of the approach is
shown on two face databases, namely, CMU-PIE and Extended Yale B, where we
achieve state of the art matching performance, along with cancelability and
high security with no unrealistic assumptions. Furthermore, the scheme can work
in both identification and verification modes.
Resolving Scale Ambiguity Via XSlit Aspect Ratio Analysis
Authors: Wei Yang, Haiting Lin, Sing Bing Kang, Jingyi Yu
Date: 2015-06-14 00:00:00
Proceeding: ICCV 2015 12
Tasks: 3D Reconstruction

In perspective cameras, images of a frontal-parallel 3D object preserve its
aspect ratio invariant to its depth. Such an invariance is useful in
photography but is unique to perspective projection. In this paper, we show
that alternative non-perspective cameras such as the crossed-slit or XSlit
cameras exhibit a different depth-dependent aspect ratio (DDAR) property that
can be used to 3D recovery. We first conduct a comprehensive analysis to
characterize DDAR, infer object depth from its AR, and model recoverable depth
range, sensitivity, and error. We show that repeated shape patterns in real
Manhattan World scenes can be used for 3D reconstruction using a single XSlit
image. We also extend our analysis to model slopes of lines. Specifically,
parallel 3D lines exhibit depth-dependent slopes (DDS) on their images which
can also be used to infer their depths. We validate our analyses using real
XSlit cameras, XSlit panoramas, and catadioptric mirrors. Experiments show that
DDAR and DDS provide important depth cues and enable effective single-image
scene reconstruction.
Contextual Dueling Bandits
Authors: Miroslav Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi
Date: 2015-02-23 00:00:00

We consider the problem of learning to choose actions using contextual
information when provided with limited feedback in the form of relative
pairwise comparisons. We study this problem in the dueling-bandits framework of
Yue et al. (2009), which we extend to incorporate context. Roughly, the
learner's goal is to find the best policy, or way of behaving, in some space of
policies, although "best" is not always so clearly defined. Here, we propose a
new and natural solution concept, rooted in game theory, called a von Neumann
winner, a randomized policy that beats or ties every other policy. We show that
this notion overcomes important limitations of existing solutions, particularly
the Condorcet winner which has typically been used in the past, but which
requires strong and often unrealistic assumptions. We then present three
efficient algorithms for online learning in our setting, and for approximating
a von Neumann winner from batch-like data. The first of these algorithms
achieves particularly low regret, even when data is adversarial, although its
time and space requirements are linear in the size of the policy space. The
other two algorithms require time and space only logarithmic in the size of the
policy space when provided access to an oracle for solving classification
problems on the space.
A General Framework for Fast Stagewise Algorithms
Authors: Ryan J. Tibshirani
Date: 2014-08-25 00:00:00
Tasks: Denoising, Image Denoising, Matrix Completion, regression

Forward stagewise regression follows a very simple strategy for constructing
a sequence of sparse regression estimates: it starts with all coefficients
equal to zero, and iteratively updates the coefficient (by a small amount
$\epsilon$) of the variable that achieves the maximal absolute inner product
with the current residual. This procedure has an interesting connection to the
lasso: under some conditions, it is known that the sequence of forward
stagewise estimates exactly coincides with the lasso path, as the step size
$\epsilon$ goes to zero. Furthermore, essentially the same equivalence holds
outside of least squares regression, with the minimization of a differentiable
convex loss function subject to an $\ell_1$ norm constraint (the stagewise
algorithm now updates the coefficient corresponding to the maximal absolute
component of the gradient).
  Even when they do not match their $\ell_1$-constrained analogues, stagewise
estimates provide a useful approximation, and are computationally appealing.
Their success in sparse modeling motivates the question: can a simple,
effective strategy like forward stagewise be applied more broadly in other
regularization settings, beyond the $\ell_1$ norm and sparsity? The current
paper is an attempt to do just this. We present a general framework for
stagewise estimation, which yields fast algorithms for problems such as
group-structured learning, matrix completion, image denoising, and more.
On the Optimality of Averaging in Distributed Statistical Learning
Authors: Jonathan Rosenblatt, Boaz Nadler
Date: 2014-07-10 00:00:00

A common approach to statistical learning with big-data is to randomly split
it among $m$ machines and learn the parameter of interest by averaging the $m$
individual estimates. In this paper, focusing on empirical risk minimization,
or equivalently M-estimation, we study the statistical error incurred by this
strategy. We consider two large-sample settings: First, a classical setting
where the number of parameters $p$ is fixed, and the number of samples per
machine $n\to\infty$. Second, a high-dimensional regime where both
$p,n\to\infty$ with $p/n \to \kappa \in (0,1)$. For both regimes and under
suitable assumptions, we present asymptotically exact expressions for this
estimation error. In the fixed-$p$ setting, under suitable assumptions, we
prove that to leading order averaging is as accurate as the centralized
solution. We also derive the second order error terms, and show that these can
be non-negligible, notably for non-linear models. The high-dimensional setting,
in contrast, exhibits a qualitatively different behavior: data splitting incurs
a first-order accuracy loss, which to leading order increases linearly with the
number of machines. The dependence of our error approximations on the number of
machines traces an interesting accuracy-complexity tradeoff, allowing the
practitioner an informed choice on the number of machines to deploy. Finally,
we confirm our theoretical analysis with several simulations.
How much is said in a microblog? A multilingual inquiry based on Weibo and Twitter
Authors: Han-Teng Liao, King-wa Fu, Scott A. Hale
Date: 2015-06-01 00:00:00

This paper presents a multilingual study on, per single post of microblog
text, (a) how much can be said, (b) how much is written in terms of characters
and bytes, and (c) how much is said in terms of information content in posts by
different organizations in different languages. Focusing on three different
languages (English, Chinese, and Japanese), this research analyses Weibo and
Twitter accounts of major embassies and news agencies. We first establish our
criterion for quantifying "how much can be said" in a digital text based on the
openly available Universal Declaration of Human Rights and the translated
subtitles from TED talks. These parallel corpora allow us to determine the
number of characters and bits needed to represent the same content in different
languages and character encodings. We then derive the amount of information
that is actually contained in microblog posts authored by selected accounts on
Weibo and Twitter. Our results confirm that languages with larger character
sets such as Chinese and Japanese contain more information per character than
English, but the actual information content contained within a microblog text
varies depending on both the type of organization and the language of the post.
We conclude with a discussion on the design implications of microblog text
limits for different languages.
Extract an essential skeleton of a character as a graph from a character image
Authors: Kazuhisa Fujita
Date: 2015-06-13 00:00:00

This paper aims to make a graph representing an essential skeleton of a character from an image that includes a machine printed or a handwritten character using growing neural gas (GNG) method and relative network graph (RNG) algorithm. The visual system in our brain can recognize printed characters and handwritten characters easily, robustly, and precisely. How does our brain robustly recognize characters? The visual processing in our brain uses the essential features of an object, such as crosses and corners. These features will be helpful for character recognition by a computer. However, extraction of the features is difficult. If the skeleton of a character is represented as a graph, we can more easily extract the features. To extract the skeleton of a character as a graph from an image, this paper proposes the new approach using GNG and RNG algorithm. I achieved to extract skeleton graphs from images including distorted, noisy, and handwritten characters.
Attacker and Defender Counting Approach for Abstract Argumentation
Authors: Fuan Pu, Jian Luo, Yulai Zhang, Guiming Luo
Date: 2015-06-13 00:00:00
Tasks: Abstract Argumentation

In Dung's abstract argumentation, arguments are either acceptable or
unacceptable, given a chosen notion of acceptability. This gives a coarse way
to compare arguments. In this paper, we propose a counting approach for a more
fine-gained assessment to arguments by counting the number of their respective
attackers and defenders based on argument graph and argument game. An argument
is more acceptable if the proponent puts forward more number of defenders for
it and the opponent puts forward less number of attackers against it. We show
that our counting model has two well-behaved properties: normalization and
convergence. Then, we define a counting semantics based on this model, and
investigate some general properties of the semantics.
Fusing Continuous-valued Medical Labels using a Bayesian Model
Authors: Tingting Zhu, Nic Dunkley, Joachim Behar, David A. Clifton, Gari. D. Clifford
Date: 2015-03-23 00:00:00
Tasks: Time Series, Time Series Analysis

With the rapid increase in volume of time series medical data available
through wearable devices, there is a need to employ automated algorithms to
label data. Examples of labels include interventions, changes in activity (e.g.
sleep) and changes in physiology (e.g. arrhythmias). However, automated
algorithms tend to be unreliable resulting in lower quality care. Expert
annotations are scarce, expensive, and prone to significant inter- and
intra-observer variance. To address these problems, a Bayesian
Continuous-valued Label Aggregator(BCLA) is proposed to provide a reliable
estimation of label aggregation while accurately infer the precision and bias
of each algorithm. The BCLA was applied to QT interval (pro-arrhythmic
indicator) estimation from the electrocardiogram using labels from the 2006
PhysioNet/Computing in Cardiology Challenge database. It was compared to the
mean, median, and a previously proposed Expectation Maximization (EM) label
aggregation approaches. While accurately predicting each labelling algorithm's
bias and precision, the root-mean-square error of the BCLA was
11.78$\pm$0.63ms, significantly outperforming the best Challenge entry
(15.37$\pm$2.13ms) as well as the EM, mean, and median voting strategies
(14.76$\pm$0.52ms, 17.61$\pm$0.55ms, and 14.43$\pm$0.57ms respectively with
$p<0.0001$).
Contamination Estimation via Convex Relaxations
Authors: Matthew L. Malloy, Scott Alfeld, Paul Barford
Date: 2015-06-13 00:00:00

Identifying anomalies and contamination in datasets is important in a wide
variety of settings. In this paper, we describe a new technique for estimating
contamination in large, discrete valued datasets. Our approach considers the
normal condition of the data to be specified by a model consisting of a set of
distributions. Our key contribution is in our approach to contamination
estimation. Specifically, we develop a technique that identifies the minimum
number of data points that must be discarded (i.e., the level of contamination)
from an empirical data set in order to match the model to within a specified
goodness-of-fit, controlled by a p-value. Appealing to results from large
deviations theory, we show a lower bound on the level of contamination is
obtained by solving a series of convex programs. Theoretical results guarantee
the bound converges at a rate of $O(\sqrt{\log(p)/p})$, where p is the size of
the empirical data set.
Evaluation of the Accuracy of the BGLemmatizer
Authors: Elena Karashtranova, Grigor Iliev, Nadezhda Borisova, Yana Chankova, Irena Atanasova
Date: 2015-06-13 00:00:00
Tasks: Lemmatization

This paper reveals the results of an analysis of the accuracy of developed
software for automatic lemmatization for the Bulgarian language. This
lemmatization software is written entirely in Java and is distributed as a GATE
plugin. Certain statistical methods are used to define the accuracy of this
software. The results of the analysis show 95% lemmatization accuracy.