pwc-archive/papers-with-abstracts huggingface dataset:
Restricted Predicates for Hypothetical Datalog
Authors: Fernando Sáenz-Pérez
Date: 2015-12-22 00:00:00
Tasks: Negation

Hypothetical Datalog is based on an intuitionistic semantics rather than on a
classical logic semantics, and embedded implications are allowed in rule
bodies. While the usual implication (i.e., the neck of a Horn clause) stands
for inferring facts, an embedded implication plays the role of assuming its
premise for deriving its consequence. A former work introduced both a formal
framework and a goal-oriented tabled implementation, allowing negation in rule
bodies. While in that work positive assumptions for both facts and rules can
occur in the premise, negative assumptions are not allowed. In this work, we
cover this subject by introducing a new concept: a restricted predicate, which
allows negative assumptions by pruning the usual semantics of a predicate. This
new setting has been implemented in the deductive system DES.
Facility Deployment Decisions through Warp Optimizaton of Regressed Gaussian Processes
Authors: Anthony Scopatz
Date: 2015-12-22 00:00:00
Tasks: Dynamic Time Warping, Gaussian Processes, regression

A method for quickly determining deployment schedules that meet a given fuel
cycle demand is presented here. This algorithm is fast enough to perform in
situ within low-fidelity fuel cycle simulators. It uses Gaussian process
regression models to predict the production curve as a function of time and the
number of deployed facilities. Each of these predictions is measured against
the demand curve using the dynamic time warping distance. The minimum distance
deployment schedule is evaluated in a full fuel cycle simulation, whose
generated production curve then informs the model on the next optimization
iteration. The method converges within five to ten iterations to a distance
that is less than one percent of the total deployable production. A
representative once-through fuel cycle is used to demonstrate the methodology
for reactor deployment.
Transformed Residual Quantization for Approximate Nearest Neighbor Search
Authors: Jiangbo Yuan, Xiuwen Liu
Date: 2015-12-22 00:00:00
Tasks: Quantization

The success of product quantization (PQ) for fast nearest neighbor search
depends on the exponentially reduced complexities of both storage and
computation with respect to the codebook size. Recent efforts have been focused
on employing sophisticated optimization strategies, or seeking more effective
models. Residual quantization (RQ) is such an alternative that holds the same
property as PQ in terms of the aforementioned complexities. In addition to
being a direct replacement of PQ, hybrids of PQ and RQ can yield more gains for
approximate nearest neighbor search. This motivated us to propose a novel
approach to optimizing RQ and the related hybrid models. With an observation of
the general randomness increase in a residual space, we propose a new strategy
that jointly learns a local transformation per residual cluster with an
ultimate goal to reduce overall quantization errors. We have shown that our
approach can achieve significantly better accuracy on nearest neighbor search
than both the original and the optimized PQ on several very large scale
benchmarks.
Predicting the Co-Evolution of Event and Knowledge Graphs
Authors: Cristóbal Esteban, Volker Tresp, Yinchong Yang, Stephan Baier, Denis Krompaß
Date: 2015-12-21 00:00:00
Tasks: Knowledge Graphs, Representation Learning

Embedding learning, a.k.a. representation learning, has been shown to be able
to model large-scale semantic knowledge graphs. A key concept is a mapping of
the knowledge graph to a tensor representation whose entries are predicted by
models using latent representations of generalized entities. Knowledge graphs
are typically treated as static: A knowledge graph grows more links when more
facts become available but the ground truth values associated with links is
considered time invariant. In this paper we address the issue of knowledge
graphs where triple states depend on time. We assume that changes in the
knowledge graph always arrive in form of events, in the sense that the events
are the gateway to the knowledge graph. We train an event prediction model
which uses both knowledge graph background information and information on
recent events. By predicting future events, we also predict likely changes in
the knowledge graph and thus obtain a model for the evolution of the knowledge
graph as well. Our experiments demonstrate that our approach performs well in a
clinical application, a recommendation engine and a sensor network application.
Addressing Complex and Subjective Product-Related Queries with Customer Reviews
Authors: Julian McAuley, Alex Yang
Date: 2015-12-21 00:00:00
Tasks: Mixture-of-Experts

Online reviews are often our first port of call when considering products and
purchases online. When evaluating a potential purchase, we may have a specific
query in mind, e.g. `will this baby seat fit in the overhead compartment of a
747?' or `will I like this album if I liked Taylor Swift's 1989?'. To answer
such questions we must either wade through huge volumes of consumer reviews
hoping to find one that is relevant, or otherwise pose our question directly to
the community via a Q/A system.
  In this paper we hope to fuse these two paradigms: given a large volume of
previously answered queries about products, we hope to automatically learn
whether a review of a product is relevant to a given query. We formulate this
as a machine learning problem using a mixture-of-experts-type framework---here
each review is an `expert' that gets to vote on the response to a particular
query; simultaneously we learn a relevance function such that `relevant'
reviews are those that vote correctly. At test time this learned relevance
function allows us to surface reviews that are relevant to new queries
on-demand. We evaluate our system, Moqa, on a novel corpus of 1.4 million
questions (and answers) and 13 million reviews. We show quantitatively that it
is effective at addressing both binary and open-ended queries, and
qualitatively that it surfaces reviews that human evaluators consider to be
relevant.
Noncrossing Ordinal Classification
Authors: Xingye Qiao
Date: 2015-05-13 00:00:00
Tasks: Classification, General Classification, Ordinal Classification

Ordinal data are often seen in real applications. Regular multicategory
classification methods are not designed for this data type and a more proper
treatment is needed. We consider a framework of ordinal classification which
pools the results from binary classifiers together. An inherent difficulty of
this framework is that the class prediction can be ambiguous due to boundary
crossing. To fix this issue, we propose a noncrossing ordinal classification
method which materializes the framework by imposing noncrossing constraints. An
asymptotic study of the proposed method is conducted. We show by simulated and
data examples that the proposed method can improve the classification
performance for ordinal data without the ambiguity caused by boundary
crossings.
Information-Theoretic Bounded Rationality
Authors: Pedro A. Ortega, Daniel A. Braun, Justin Dyer, Kee-Eung Kim, Naftali Tishby
Date: 2015-12-21 00:00:00
Tasks: Decision Making, Reinforcement Learning

Bounded rationality, that is, decision-making and planning under resource
limitations, is widely regarded as an important open problem in artificial
intelligence, reinforcement learning, computational neuroscience and economics.
This paper offers a consolidated presentation of a theory of bounded
rationality based on information-theoretic ideas. We provide a conceptual
justification for using the free energy functional as the objective function
for characterizing bounded-rational decisions. This functional possesses three
crucial properties: it controls the size of the solution space; it has Monte
Carlo planners that are exact, yet bypass the need for exhaustive search; and
it captures model uncertainty arising from lack of evidence or from interacting
with other agents having unknown intentions. We discuss the single-step
decision-making case, and show how to extend it to sequential decisions using
equivalence transformations. This extension yields a very general class of
decision problems that encompass classical decision rules (e.g. EXPECTIMAX and
MINIMAX) as limit cases, as well as trust- and risk-sensitive planning.
Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis
Authors: Longqi Yang, Cheng-Kang Hsieh, Deborah Estrin
Date: 2015-12-21 00:00:00
Tasks: Classification, General Classification, Metric Learning

User preference profiling is an important task in modern online social
networks (OSN). With the proliferation of image-centric social platforms, such
as Pinterest, visual contents have become one of the most informative data
streams for understanding user preferences. Traditional approaches usually
treat visual content analysis as a general classification problem where one or
more labels are assigned to each image. Although such an approach simplifies
the process of image analysis, it misses the rich context and visual cues that
play an important role in people's perception of images. In this paper, we
explore the possibilities of learning a user's latent visual preferences
directly from image contents. We propose a distance metric learning method
based on Deep Convolutional Neural Networks (CNN) to directly extract
similarity information from visual contents and use the derived distance metric
to mine individual users' fine-grained visual preferences. Through our
preliminary experiments using data from 5,790 Pinterest users, we show that
even for the images within the same category, each user possesses distinct and
individually-identifiable visual preferences that are consistent over their
lifetime. Our results underscore the untapped potential of finer-grained visual
preference profiling in understanding users' preferences.
Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition
Authors: Skyler Seto, Wenyu Zhang, Yichen Zhou
Date: 2015-12-21 00:00:00
Tasks: Activity Recognition, Dynamic Time Warping, General Classification, Human Activity Recognition, Time Series, Time Series Analysis, Time Series Classification

Accurate and computationally efficient means for classifying human activities
have been the subject of extensive research efforts. Most current research
focuses on extracting complex features to achieve high classification accuracy.
We propose a template selection approach based on Dynamic Time Warping, such
that complex feature extraction and domain knowledge is avoided. We demonstrate
the predictive capability of the algorithm on both simulated and real
smartphone data.
Multilinear Subspace Clustering
Authors: Eric Kernfeld, Nathan Majumder, Shuchin Aeron, Misha Kilmer
Date: 2015-12-21 00:00:00
Tasks: Clustering

In this paper we present a new model and an algorithm for unsupervised
clustering of 2-D data such as images. We assume that the data comes from a
union of multilinear subspaces (UOMS) model, which is a specific structured
case of the much studied union of subspaces (UOS) model. For segmentation under
this model, we develop Multilinear Subspace Clustering (MSC) algorithm and
evaluate its performance on the YaleB and Olivietti image data sets. We show
that MSC is highly competitive with existing algorithms employing the UOS model
in terms of clustering performance while enjoying improvement in computational
complexity.
Sparse Coding with Fast Image Alignment via Large Displacement Optical Flow
Authors: Xiaoxia Sun, Nasser M. Nasrabadi, Trac. D. Tran
Date: 2015-12-21 00:00:00
Tasks: Dictionary Learning, image-classification, Image Classification, Optical Flow Estimation

Sparse representation-based classifiers have shown outstanding accuracy and
robustness in image classification tasks even with the presence of intense
noise and occlusion. However, it has been discovered that the performance
degrades significantly either when test image is not aligned with the
dictionary atoms or the dictionary atoms themselves are not aligned with each
other, in which cases the sparse linear representation assumption fails. In
this paper, having both training and test images misaligned, we introduce a
novel sparse coding framework that is able to efficiently adapt the dictionary
atoms to the test image via large displacement optical flow. In the proposed
algorithm, every dictionary atom is automatically aligned with the input image
and the sparse code is then recovered using the adapted dictionary atoms. A
corresponding supervised dictionary learning algorithm is also developed for
the proposed framework. Experimental results on digit datasets recognition
verify the efficacy and robustness of the proposed algorithm.
Specifying and Staging Mixed-Initiative Dialogs with Program Generation and Transformation
Authors: Saverio Perugini
Date: 2011-08-02 00:00:00

Specifying and implementing flexible human-computer dialogs, such as those
used in kiosks and smart phone apps, is challenging because of the numerous and
varied directions in which each user might steer a dialog. The objective of
this research is to improve dialog specification and implementation. To do so
we enriched a notation based on concepts from programming languages, especially
partial evaluation, for specifying a variety of unsolicited reporting,
mixed-initiative dialogs in a concise representation that serves as a design
for dialog implementation. We also built a dialog mining system that extracts a
specification in this notation from requirements. To demonstrate that such a
specification provides a design for dialog implementation, we built a system
that automatically generates an implementation of the dialog, called a stager,
from it. These two components constitute a dialog modeling toolkit that
automates dialog specification and implementation. These results provide a
proof of concept and demonstrate the study of dialog specification and
implementation from a programming languages perspective. The ubiquity of
dialogs in domains such as travel, education, and health care combined with the
demand for smart phone apps provide a landscape for further investigation of
these results.
Weakly-Supervised Alignment of Video With Text
Authors: Piotr Bojanowski, Rémi Lajugie, Edouard Grave, Francis Bach, Ivan Laptev, Jean Ponce, Cordelia Schmid
Date: 2015-05-22 00:00:00
Proceeding: ICCV 2015 12
Tasks: Sentence

Suppose that we are given a set of videos, along with natural language
descriptions in the form of multiple sentences (e.g., manual annotations, movie
scripts, sport summaries etc.), and that these sentences appear in the same
temporal order as their visual counterparts. We propose in this paper a method
for aligning the two modalities, i.e., automatically providing a time stamp for
every sentence. Given vectorial features for both video and text, we propose to
cast this task as a temporal assignment problem, with an implicit linear
mapping between the two feature modalities. We formulate this problem as an
integer quadratic program, and solve its continuous convex relaxation using an
efficient conditional gradient algorithm. Several rounding procedures are
proposed to construct the final integer solution. After demonstrating
significant improvements over the state of the art on the related task of
aligning video with symbolic labels [7], we evaluate our method on a
challenging dataset of videos with associated textual descriptions [36], using
both bag-of-words and continuous representations for text.
The 2015 Sheffield System for Transcription of Multi-Genre Broadcast Media
Authors: Oscar Saz, Mortaza Doulaty, Salil Deena, Rosanna Milner, Raymond W. M. Ng, Madina Hasan, Yu-Lan Liu, Thomas Hain
Date: 2015-12-21 00:00:00
Tasks: Acoustic Modelling, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modelling, speech-recognition, Speech Recognition

We describe the University of Sheffield system for participation in the 2015
Multi-Genre Broadcast (MGB) challenge task of transcribing multi-genre
broadcast shows. Transcription was one of four tasks proposed in the MGB
challenge, with the aim of advancing the state of the art of automatic speech
recognition, speaker diarisation and automatic alignment of subtitles for
broadcast media. Four topics are investigated in this work: Data selection
techniques for training with unreliable data, automatic speech segmentation of
broadcast media shows, acoustic modelling and adaptation in highly variable
environments, and language modelling of multi-genre shows. The final system
operates in multiple passes, using an initial unadapted decoding stage to
refine segmentation, followed by three adapted passes: a hybrid DNN pass with
input features normalised by speaker-based cepstral normalisation, another
hybrid stage with input features normalised by speaker feature-MLLR
transformations, and finally a bottleneck-based tandem stage with noise and
speaker factorisation. The combination of these three system outputs provides a
final error rate of 27.5% on the official development set, consisting of 47
multi-genre shows.
Constrained Sampling and Counting: Universal Hashing Meets SAT Solving
Authors: Kuldeep S. Meel, Moshe Vardi, Supratik Chakraborty, Daniel J. Fremont, Sanjit A. Seshia, Dror Fried, Alexander Ivrii, Sharad Malik
Date: 2015-12-21 00:00:00

Constrained sampling and counting are two fundamental problems in artificial
intelligence with a diverse range of applications, spanning probabilistic
reasoning and planning to constrained-random verification. While the theory of
these problems was thoroughly investigated in the 1980s, prior work either did
not scale to industrial size instances or gave up correctness guarantees to
achieve scalability. Recently, we proposed a novel approach that combines
universal hashing and SAT solving and scales to formulas with hundreds of
thousands of variables without giving up correctness guarantees. This paper
provides an overview of the key ingredients of the approach and discusses
challenges that need to be overcome to handle larger real-world instances.
Spatial Phase-Sweep: Increasing temporal resolution of transient imaging using a light source array
Authors: Ryuichi Tadano, Adithya Kumar Pediredla, Kaushik Mitra, Ashok Veeraraghavan
Date: 2015-12-21 00:00:00

Transient imaging or light-in-flight techniques capture the propagation of an
ultra-short pulse of light through a scene, which in effect captures the
optical impulse response of the scene. Recently, it has been shown that we can
capture transient images using commercially available Time-of-Flight (ToF)
systems such as Photonic Mixer Devices (PMD). In this paper, we propose
`spatial phase-sweep', a technique that exploits the speed of light to increase
the temporal resolution beyond the 100 picosecond limit imposed by current
electronics. Spatial phase-sweep uses a linear array of light sources with
spatial separation of about 3 mm between them, thereby resulting in a time
shift of about 10 picoseconds, which translates into 100 Gfps of transient
imaging in theory. We demonstrate a prototype and transient imaging results
using spatial phase-sweep.
Remote Health Coaching System and Human Motion Data Analysis for Physical Therapy with Microsoft Kinect
Authors: Qifei Wang, Gregorij Kurillo, Ferda Ofli, Ruzena Bajcsy
Date: 2015-12-21 00:00:00

This paper summarizes the recent progress we have made for the computer
vision technologies in physical therapy with the accessible and affordable
devices. We first introduce the remote health coaching system we build with
Microsoft Kinect. Since the motion data captured by Kinect is noisy, we
investigate the data accuracy of Kinect with respect to the high accuracy
motion capture system. We also propose an outlier data removal algorithm based
on the data distribution. In order to generate the kinematic parameter from the
noisy data captured by Kinect, we propose a kinematic filtering algorithm based
on Unscented Kalman Filter and the kinematic model of human skeleton. The
proposed algorithm can obtain smooth kinematic parameter with reduced noise
compared to the kinematic parameter generated from the raw motion data from
Kinect.
Reading Scene Text in Deep Convolutional Sequences
Authors: Pan He, Weilin Huang, Yu Qiao, Chen Change Loy, Xiaoou Tang
Date: 2015-06-14 00:00:00
Tasks: Scene Text Recognition

We develop a Deep-Text Recurrent Network (DTRN) that regards scene text
reading as a sequence labelling problem. We leverage recent advances of deep
convolutional neural networks to generate an ordered high-level sequence from a
whole word image, avoiding the difficult character segmentation problem. Then a
deep recurrent model, building on long short-term memory (LSTM), is developed
to robustly recognize the generated CNN sequences, departing from most existing
approaches recognising each character independently. Our model has a number of
appealing properties in comparison to existing scene text recognition methods:
(i) It can recognise highly ambiguous words by leveraging meaningful context
information, allowing it to work reliably without either pre- or
post-processing; (ii) the deep CNN feature is robust to various image
distortions; (iii) it retains the explicit order information in word image,
which is essential to discriminate word strings; (iv) the model does not depend
on pre-defined dictionary, and it can process unknown words and arbitrary
strings. Codes for the DTRN will be available.
Behavioral Modeling for Churn Prediction: Early Indicators and Accurate Predictors of Custom Defection and Loyalty
Authors: Muhammad R. Khan, Johua Manoj, Anikate Singh, Joshua Blumenstock
Date: 2015-12-20 00:00:00
Tasks: Feature Engineering, feature selection

Churn prediction, or the task of identifying customers who are likely to
discontinue use of a service, is an important and lucrative concern of firms in
many different industries. As these firms collect an increasing amount of
large-scale, heterogeneous data on the characteristics and behaviors of
customers, new methods become possible for predicting churn. In this paper, we
present a unified analytic framework for detecting the early warning signs of
churn, and assigning a "Churn Score" to each customer that indicates the
likelihood that the particular individual will churn within a predefined amount
of time. This framework employs a brute force approach to feature engineering,
then winnows the set of relevant attributes via feature selection, before
feeding the final feature-set into a suite of supervised learning algorithms.
Using several terabytes of data from a large mobile phone network, our method
identifies several intuitive - and a few surprising - early warning signs of
churn, and our best model predicts whether a subscriber will churn with 89.4%
accuracy.
Towards Integrated Glance To Restructuring in Combinatorial Optimization
Authors: Mark Sh. Levin
Date: 2015-12-20 00:00:00
Tasks: Clustering, Combinatorial Optimization, Multiple-choice

The paper focuses on a new class of combinatorial problems which consists in
restructuring of solutions (as sets/structures) in combinatorial optimization.
Two main features of the restructuring process are examined: (i) a cost of the
restructuring, (ii) a closeness to a goal solution. Three types of the
restructuring problems are under study: (a) one-stage structuring, (b)
multi-stage structuring, and (c) structuring over changed element set.
One-criterion and multicriteria problem formulations can be considered. The
restructuring problems correspond to redesign (improvement, upgrade) of modular
systems or solutions. The restructuring approach is described and illustrated
(problem statements, solving schemes, examples) for the following combinatorial
optimization problems: knapsack problem, multiple choice problem, assignment
problem, spanning tree problems, clustering problem, multicriteria ranking
(sorting) problem, morphological clique problem. Numerical examples illustrate
the restructuring problems and solving schemes.