pwc-archive/papers-with-abstracts huggingface dataset:
Church: a language for generative models
Authors: Noah Goodman, Vikash Mansinghka, Daniel M. Roy, Keith Bonawitz, Joshua B. Tenenbaum
Date: 2012-06-13 00:00:00
Tasks: Clustering

We introduce Church, a universal language for describing stochastic
generative processes. Church is based on the Lisp model of lambda calculus,
containing a pure Lisp as its deterministic subset. The semantics of Church is
defined in terms of evaluation histories and conditional distributions on such
histories. Church also includes a novel language construct, the stochastic
memoizer, which enables simple description of many complex non-parametric
models. We illustrate language features through several examples, including: a
generalized Bayes net in which parameters cluster over trials, infinite PCFGs,
planning by inference, and various non-parametric clustering models. Finally,
we show how to implement query on any Church program, exactly and
approximately, using Monte Carlo techniques.
Abduction and Dialogical Proof in Argumentation and Logic Programming
Authors: Richard Booth, Dov Gabbay, Souhila Kaci, Tjitze Rienstra, Leendert van der Torre
Date: 2014-07-15 00:00:00
Tasks: Abstract Argumentation

We develop a model of abduction in abstract argumentation, where changes to
an argumentation framework act as hypotheses to explain the support of an
observation. We present dialogical proof theories for the main decision
problems (i.e., finding hypothe- ses that explain skeptical/credulous support)
and we show that our model can be instantiated on the basis of abductive logic
programs.
Growing Regression Forests by Classification: Applications to Object Pose Estimation
Authors: Kota Hara, Rama Chellappa
Date: 2013-12-22 00:00:00
Tasks: General Classification, Head Pose Estimation, Pose Estimation, regression

In this work, we propose a novel node splitting method for regression trees
and incorporate it into the regression forest framework. Unlike traditional
binary splitting, where the splitting rule is selected from a predefined set of
binary splitting rules via trial-and-error, the proposed node splitting method
first finds clusters of the training data which at least locally minimize the
empirical loss without considering the input space. Then splitting rules which
preserve the found clusters as much as possible are determined by casting the
problem into a classification problem. Consequently, our new node splitting
method enjoys more freedom in choosing the splitting rules, resulting in more
efficient tree structures. In addition to the Euclidean target space, we
present a variant which can naturally deal with a circular target space by the
proper use of circular statistics. We apply the regression forest employing our
node splitting to head pose estimation (Euclidean target space) and car
direction estimation (circular target space) and demonstrate that the proposed
method significantly outperforms state-of-the-art methods (38.5% and 22.5%
error reduction respectively).
Part-based R-CNNs for Fine-grained Category Detection
Authors: Ning Zhang, Jeff Donahue, Ross Girshick, Trevor Darrell
Date: 2014-07-15 00:00:00
Tasks: Fine-Grained Image Classification, Object, object-detection, Object Detection

Semantic part localization can facilitate fine-grained categorization by
explicitly isolating subtle appearance differences associated with specific
object parts. Methods for pose-normalized representations have been proposed,
but generally presume bounding box annotations at test time due to the
difficulty of object detection. We propose a model for fine-grained
categorization that overcomes these limitations by leveraging deep
convolutional features computed on bottom-up region proposals. Our method
learns whole-object and part detectors, enforces learned geometric constraints
between them, and predicts a fine-grained category from a pose-normalized
representation. Experiments on the Caltech-UCSD bird dataset confirm that our
method outperforms state-of-the-art fine-grained categorization methods in an
end-to-end evaluation without requiring a bounding box at test time.
Controlled Natural Language Processing as Answer Set Programming: an Experiment
Authors: Rolf Schwitter
Date: 2014-07-15 00:00:00

Most controlled natural languages (CNLs) are processed with the help of a
pipeline architecture that relies on different software components. We
investigate in this paper in an experimental way how well answer set
programming (ASP) is suited as a unifying framework for parsing a CNL, deriving
a formal representation for the resulting syntax trees, and for reasoning with
that representation. We start from a list of input tokens in ASP notation and
show how this input can be transformed into a syntax tree using an ASP grammar
and then into reified ASP rules in form of a set of facts. These facts are then
processed by an ASP meta-interpreter that allows us to infer new knowledge.
A semantic network-based evolutionary algorithm for computational creativity
Authors: Atilim Gunes Baydin, Ramon Lopez de Mantaras, Santiago Ontanon
Date: 2014-04-30 00:00:00
Tasks: Analogical Similarity, global-optimization

We introduce a novel evolutionary algorithm (EA) with a semantic
network-based representation. For enabling this, we establish new formulations
of EA variation operators, crossover and mutation, that we adapt to work on
semantic networks. The algorithm employs commonsense reasoning to ensure all
operations preserve the meaningfulness of the networks, using ConceptNet and
WordNet knowledge bases. The algorithm can be interpreted as a novel memetic
algorithm (MA), given that (1) individuals represent pieces of information that
undergo evolution, as in the original sense of memetics as it was introduced by
Dawkins; and (2) this is different from existing MA, where the word "memetic"
has been used as a synonym for local refinement after global optimization. For
evaluating the approach, we introduce an analogical similarity-based fitness
measure that is computed through structure mapping. This setup enables the
open-ended generation of networks analogous to a given base network.
Imparo is complete by inverse subsumption
Authors: David Toth
Date: 2014-07-14 00:00:00
Tasks: Inductive logic programming

In Inverse subsumption for complete explanatory induction Yamamoto et al.
investigate which inductive logic programming systems can learn a correct
hypothesis $H$ by using the inverse subsumption instead of inverse entailment.
We prove that inductive logic programming system Imparo is complete by inverse
subsumption for learning a correct definite hypothesis $H$ wrt the definite
background theory $B$ and ground atomic examples $E$, by establishing that
there exists a connected theory $T$ for $B$ and $E$ such that $H$ subsumes $T$.
Non-Monotonic Reasoning and Story Comprehension
Authors: Irene-Anna Diakidoy, Antonis Kakas, Loizos Michael, Rob Miller
Date: 2014-07-14 00:00:00
Tasks: Common Sense Reasoning, Reading Comprehension, World Knowledge

This paper develops a Reasoning about Actions and Change framework integrated
with Default Reasoning, suitable as a Knowledge Representation and Reasoning
framework for Story Comprehension. The proposed framework, which is guided
strongly by existing knowhow from the Psychology of Reading and Comprehension,
is based on the theory of argumentation from AI. It uses argumentation to
capture appropriate solutions to the frame, ramification and qualification
problems and generalizations of these problems required for text comprehension.
In this first part of the study the work concentrates on the central problem of
integration (or elaboration) of the explicit information from the narrative in
the text with the implicit (in the readers mind) common sense world knowledge
pertaining to the topic(s) of the story given in the text. We also report on
our empirical efforts to gather background common sense world knowledge used by
humans when reading a story and to evaluate, through a prototype system, the
ability of our approach to capture both the majority and the variability of
understanding of a story by the human readers in the experiments.
A Framework for Exploring Non-Linear Functional Connectivity and Causality in the Human Brain: Mutual Connectivity Analysis (MCA) of Resting-State Functional MRI with Convergent Cross-Mapping and Non-Metric Clustering
Authors: Axel Wismüller, Xixi Wang, Adora M. DSouza, Mahesh B. Nagarajan
Date: 2014-07-14 00:00:00
Tasks: Clustering, Functional Connectivity, Time Series Analysis

We present a computational framework for analysis and visualization of
non-linear functional connectivity in the human brain from resting state
functional MRI (fMRI) data for purposes of recovering the underlying network
community structure and exploring causality between network components. Our
proposed methodology of non-linear mutual connectivity analysis (MCA) involves
two computational steps. First, the pair-wise cross-prediction performance
between resting state fMRI pixel time series within the brain is evaluated. The
underlying network structure is subsequently recovered from the affinity matrix
constructed through MCA using non-metric network partitioning/clustering with
the so-called Louvain method. We demonstrate our methodology in the task of
identifying regions of the motor cortex associated with hand movement on
resting state fMRI data acquired from eight slice locations in four subjects.
For comparison, we also localized regions of the motor cortex through a
task-based fMRI sequence involving a finger-tapping stimulus paradigm. Finally,
we integrate convergent cross mapping (CCM) into the first step of MCA for
investigating causality between regions of the motor cortex. Results regarding
causation between regions of the motor cortex revealed a significant
directional variability and were not readily interpretable in a consistent
manner across all subjects. However, our results on whole-slice fMRI analysis
demonstrate that MCA-based model-free recovery of regions associated with the
primary motor cortex and supplementary motor area are in close agreement with
localization of similar regions achieved with a task-based fMRI acquisition.
Thus, we conclude that our computational framework MCA can extract and
visualize valuable information concerning the underlying network structure and
causation between different regions of the brain in resting state fMRI.
Benchmarking Named Entity Disambiguation approaches for Streaming Graphs
Authors: Sutanay Choudhury, Chase Dowling
Date: 2014-07-14 00:00:00
Tasks: Benchmarking, Entity Disambiguation, Entity Linking, Knowledge Base Population

Named Entity Disambiaguation (NED) is a central task for applications dealing
with natural language text. Assume that we have a graph based knowledge base
(subsequently referred as Knowledge Graph) where nodes represent various real
world entities such as people, location, organization and concepts. Given data
sources such as social media streams and web pages Entity Linking is the task
of mapping named entities that are extracted from the data to those present in
the Knowledge Graph. This is an inherently difficult task due to several
reasons. Almost all these data sources are generated without any formal
ontology; the unstructured nature of the input, limited context and the
ambiguity involved when multiple entities are mapped to the same name make this
a hard task. This report looks at two state of the art systems employing two
distinctive approaches: graph based Accurate Online Disambiguation of Entities
(AIDA) and Mined Evidence Named Entity Disambiguation (MENED), which employs a
statistical inference approach. We compare both approaches using the data set
and queries provided by the Knowledge Base Population (KBP) track at 2011 NIST
Text Analytics Conference (TAC). This report begins with an overview of the
respective approaches, followed by detailed description of the experimental
setup. It concludes with our findings from the benchmarking exercise.
Finding representative sets of optimizations for adaptive multiversioning applications
Authors: Lianjie Luo, Yang Chen, Chengyong Wu, Shun Long, Grigori Fursin
Date: 2014-07-14 00:00:00

Iterative compilation is a widely adopted technique to optimize programs for
different constraints such as performance, code size and power consumption in
rapidly evolving hardware and software environments. However, in case of
statically compiled programs, it is often restricted to optimizations for a
specific dataset and may not be applicable to applications that exhibit
different run-time behavior across program phases, multiple datasets or when
executed in heterogeneous, reconfigurable and virtual environments. Several
frameworks have been recently introduced to tackle these problems and enable
run-time optimization and adaptation for statically compiled programs based on
static function multiversioning and monitoring of online program behavior. In
this article, we present a novel technique to select a minimal set of
representative optimization variants (function versions) for such frameworks
while avoiding performance loss across available datasets and code-size
explosion. We developed a novel mapping mechanism using popular decision tree
or rule induction based machine learning techniques to rapidly select best code
versions at run-time based on dataset features and minimize selection overhead.
These techniques enable creation of self-tuning static binaries or libraries
adaptable to changing behavior and environments at run-time using staged
compilation that do not require complex recompilation frameworks while
effectively outperforming traditional single-version non-adaptable code.
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection
Authors: Alejandro González, Sebastian Ramos, David Vázquez, Antonio M. López, Jaume Amores
Date: 2014-07-14 00:00:00
Tasks: Pedestrian Detection

Pedestrian classifiers decide which image windows contain a pedestrian. In
practice, such classifiers provide a relatively high response at neighbor
windows overlapping a pedestrian, while the responses around potential false
positives are expected to be lower. An analogous reasoning applies for image
sequences. If there is a pedestrian located within a frame, the same pedestrian
is expected to appear close to the same location in neighbor frames. Therefore,
such a location has chances of receiving high classification scores during
several frames, while false positives are expected to be more spurious. In this
paper we propose to exploit such correlations for improving the accuracy of
base pedestrian classifiers. In particular, we propose to use two-stage
classifiers which not only rely on the image descriptors required by the base
classifiers but also on the response of such base classifiers in a given
spatiotemporal neighborhood. More specifically, we train pedestrian classifiers
using a stacked sequential learning (SSL) paradigm. We use a new pedestrian
dataset we have acquired from a car to evaluate our proposal at different frame
rates. We also test on a well known dataset: Caltech. The obtained results show
that our SSL proposal boosts detection accuracy significantly with a minimal
impact on the computational cost. Interestingly, SSL improves more the accuracy
at the most dangerous situations, i.e. when a pedestrian is close to the
camera.
Finding Motif Sets in Time Series
Authors: Anthony Bagnall, Jon Hills, Jason Lines
Date: 2014-07-14 00:00:00
Tasks: Time Series, Time Series Analysis

Time-series motifs are representative subsequences that occur frequently in a
time series; a motif set is the set of subsequences deemed to be instances of a
given motif. We focus on finding motif sets. Our motivation is to detect motif
sets in household electricity-usage profiles, representing repeated patterns of
household usage.
  We propose three algorithms for finding motif sets. Two are greedy algorithms
based on pairwise comparison, and the third uses a heuristic measure of set
quality to find the motif set directly. We compare these algorithms on
simulated datasets and on electricity-usage data. We show that Scan MK, the
simplest way of using the best-matching pair to find motif sets, is less
accurate on our synthetic data than Set Finder and Cluster MK, although the
latter is very sensitive to parameter settings. We qualitatively analyse the
outputs for the electricity-usage data and demonstrate that both Scan MK and
Set Finder can discover useful motif sets in such data.
Memristor models for machine learning
Authors: Juan Pablo Carbajal, Joni Dambre, Michiel Hermans, Benjamin Schrauwen
Date: 2014-06-09 00:00:00
Tasks: BIG-bench Machine Learning

In the quest for alternatives to traditional CMOS, it is being suggested that
digital computing efficiency and power can be improved by matching the
precision to the application. Many applications do not need the high precision
that is being used today. In particular, large gains in area- and power
efficiency could be achieved by dedicated analog realizations of approximate
computing engines. In this work, we explore the use of memristor networks for
analog approximate computation, based on a machine learning framework called
reservoir computing. Most experimental investigations on the dynamics of
memristors focus on their nonvolatile behavior. Hence, the volatility that is
present in the developed technologies is usually unwanted and it is not
included in simulation models. In contrast, in reservoir computing, volatility
is not only desirable but necessary. Therefore, in this work, we propose two
different ways to incorporate it into memristor simulation models. The first is
an extension of Strukov's model and the second is an equivalent Wiener model
approximation. We analyze and compare the dynamical properties of these models
and discuss their implications for the memory and the nonlinear processing
capacity of memristor networks. Our results indicate that device variability,
increasingly causing problems in traditional computer design, is an asset in
the context of reservoir computing. We conclude that, although both models
could lead to useful memristor based reservoir computing systems, their
computational performance will differ. Therefore, experimental modeling
research is required for the development of accurate volatile memristor models.
An Enhancement Neighborhood connected Segmentation for 2D-Cellular Image
Authors: Mohammed M. Abdelsamea
Date: 2014-07-14 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

A good segmentation result depends on a set of "correct" choice for the
seeds. When the input images are noisy, the seeds may fall on atypical pixels
that are not representative of the region statistics. This can lead to
erroneous segmentation results. In this paper, an automatic seeded region
growing algorithm is proposed for cellular image segmentation. First, the
regions of interest (ROIs) extracted from the preprocessed image. Second, the
initial seeds are automatically selected based on ROIs extracted from the
image. Third, the most reprehensive seeds are selected using a machine learning
algorithm. Finally, the cellular image is segmented into regions where each
region corresponds to a seed. The aim of the proposed is to automatically
extract the Region of Interests (ROI) from in the cellular images in terms of
overcoming the explosion, under segmentation and over segmentation problems.
Experimental results show that the proposed algorithm can improve the segmented
image and the segmented results are less noisy as compared to some existing
algorithms.
Self Organization Map based Texture Feature Extraction for Efficient Medical Image Categorization
Authors: Marghny H. Mohamed, Mohammed M. Abdelsamea
Date: 2014-07-14 00:00:00
Tasks: feature selection, Image Categorization

Texture is one of the most important properties of visual surface that helps
in discriminating one object from another or an object from background. The
self-organizing map (SOM) is an excellent tool in exploratory phase of data
mining. It projects its input space on prototypes of a low-dimensional regular
grid that can be effectively utilized to visualize and explore properties of
the data. This paper proposes an enhancement extraction method for accurate
extracting features for efficient image representation it based on SOM neural
network. In this approach, we apply three different partitioning approaches as
a region of interested (ROI) selection methods for extracting different
accurate textural features from medical image as a primary step of our
extraction method. Fisherfaces feature selection is used, for selecting
discriminated features form extracted textural features. Experimental result
showed the high accuracy of medical image categorization with our proposed
extraction method. Experiments held on Mammographic Image Analysis Society
(MIAS) dataset.
Toward Network-based Keyword Extraction from Multitopic Web Documents
Authors: Sabina Šišović, Sanda Martinčić-Ipšić, Ana Meštrović
Date: 2014-07-14 00:00:00
Tasks: Keyword Extraction, Sentence

In this paper we analyse the selectivity measure calculated from the complex
network in the task of the automatic keyword extraction. Texts, collected from
different web sources (portals, forums), are represented as directed and
weighted co-occurrence complex networks of words. Words are nodes and links are
established between two nodes if they are directly co-occurring within the
sentence. We test different centrality measures for ranking nodes - keyword
candidates. The promising results are achieved using the selectivity measure.
Then we propose an approach which enables extracting word pairs according to
the values of the in/out selectivity and weight measures combined with
filtering.
On the Power of Adaptivity in Matrix Completion and Approximation
Authors: Akshay Krishnamurthy, Aarti Singh
Date: 2014-07-14 00:00:00
Tasks: Matrix Completion

We consider the related tasks of matrix completion and matrix approximation
from missing data and propose adaptive sampling procedures for both problems.
We show that adaptive sampling allows one to eliminate standard incoherence
assumptions on the matrix row space that are necessary for passive sampling
procedures. For exact recovery of a low-rank matrix, our algorithm judiciously
selects a few columns to observe in full and, with few additional measurements,
projects the remaining columns onto their span. This algorithm exactly recovers
an $n \times n$ rank $r$ matrix using $O(nr\mu_0 \log^2(r))$ observations,
where $\mu_0$ is a coherence parameter on the column space of the matrix. In
addition to completely eliminating any row space assumptions that have pervaded
the literature, this algorithm enjoys a better sample complexity than any
existing matrix completion algorithm. To certify that this improvement is due
to adaptive sampling, we establish that row space coherence is necessary for
passive sampling algorithms to achieve non-trivial sample complexity bounds.
  For constructing a low-rank approximation to a high-rank input matrix, we
propose a simple algorithm that thresholds the singular values of a zero-filled
version of the input matrix. The algorithm computes an approximation that is
nearly as good as the best rank-$r$ approximation using $O(nr\mu \log^2(n))$
samples, where $\mu$ is a slightly different coherence parameter on the matrix
columns. Again we eliminate assumptions on the row space.