pwc-archive/papers-with-abstracts huggingface dataset:
Resolution of Difficult Pronouns Using the ROSS Method
Authors: Glenn R. Hofford
Date: 2014-11-15 00:00:00
Tasks: Entity Resolution, Natural Language Understanding

A new natural language understanding method for disambiguation of difficult
pronouns is described. Difficult pronouns are those pronouns for which a level
of world or domain knowledge is needed in order to perform anaphoral or other
types of resolution. Resolution of difficult pronouns may in some cases require
a prior step involving the application of inference to a situation that is
represented by the natural language text. A general method is described: it
performs entity resolution and pronoun resolution. An extension to the general
pronoun resolution method performs inference as an embedded commonsense
reasoning method. The general method and the embedded method utilize features
of the ROSS representational scheme; in particular the methods use ROSS
ontology classes and the ROSS situation model. The overall method is a working
solution that solves the following Winograd schemas: a) trophy and suitcase, b)
person lifts person, c) person pays detective, and d) councilmen and
demonstrators.
Anisotropic Agglomerative Adaptive Mean-Shift
Authors: Rahul Sawhney, Henrik I. Christensen, Gary R. Bradski
Date: 2014-11-15 00:00:00
Tasks: Clustering

Mean Shift today, is widely used for mode detection and clustering. The
technique though, is challenged in practice due to assumptions of isotropicity
and homoscedasticity. We present an adaptive Mean Shift methodology that allows
for full anisotropic clustering, through unsupervised local bandwidth
selection. The bandwidth matrices evolve naturally, adapting locally through
agglomeration, and in turn guiding further agglomeration. The online
methodology is practical and effecive for low-dimensional feature spaces,
preserving better detail and clustering salience. Additionally, conventional
Mean Shift either critically depends on a per instance choice of bandwidth, or
relies on offline methods which are inflexible and/or again data instance
specific. The presented approach, due to its adaptive design, also alleviates
this issue - with a default form performing generally well. The methodology
though, allows for effective tuning of results.
Deep Deconvolutional Networks for Scene Parsing
Authors: Rahul Mohan
Date: 2014-11-15 00:00:00
Tasks: Scene Parsing, Superpixels

Scene parsing is an important and challenging prob- lem in computer vision.
It requires labeling each pixel in an image with the category it belongs to.
Tradition- ally, it has been approached with hand-engineered features from
color information in images. Recently convolutional neural networks (CNNs),
which automatically learn hierar- chies of features, have achieved record
performance on the task. These approaches typically include a post-processing
technique, such as superpixels, to produce the final label- ing. In this paper,
we propose a novel network architecture that combines deep deconvolutional
neural networks with CNNs. Our experiments show that deconvolutional neu- ral
networks are capable of learning higher order image structure beyond edge
primitives in comparison to CNNs. The new network architecture is employed for
multi-patch training, introduced as part of this work. Multi-patch train- ing
makes it possible to effectively learn spatial priors from scenes. The proposed
approach yields state-of-the-art per- formance on four scene parsing datasets,
namely Stanford Background, SIFT Flow, CamVid, and KITTI. In addition, our
system has the added advantage of having a training system that can be
completely automated end-to-end with- out requiring any post-processing.
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Authors: Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Zhouyue Pi, Bhaskar D. Rao
Date: 2014-04-21 00:00:00
Tasks: Brain Computer Interface, compressed sensing, Data Compression, EEG, Electroencephalogram (EEG)

Energy consumption is an important issue in continuous wireless
telemonitoring of physiological signals. Compressed sensing (CS) is a promising
framework to address it, due to its energy-efficient data compression
procedure. However, most CS algorithms have difficulty in data recovery due to
non-sparsity characteristic of many physiological signals. Block sparse
Bayesian learning (BSBL) is an effective approach to recover such signals with
satisfactory recovery quality. However, it is time-consuming in recovering
multichannel signals, since its computational load almost linearly increases
with the number of channels.
  This work proposes a spatiotemporal sparse Bayesian learning algorithm to
recover multichannel signals simultaneously. It not only exploits temporal
correlation within each channel signal, but also exploits inter-channel
correlation among different channel signals. Furthermore, its computational
load is not significantly affected by the number of channels. The proposed
algorithm was applied to brain computer interface (BCI) and EEG-based driver's
drowsiness estimation. Results showed that the algorithm had both better
recovery performance and much higher speed than BSBL. Particularly, the
proposed algorithm ensured that the BCI classification and the drowsiness
estimation had little degradation even when data were compressed by 80%, making
it very suitable for continuous wireless telemonitoring of multichannel
signals.
GASP : Geometric Association with Surface Patches
Authors: Rahul Sawhney, Fuxin Li, Henrik I. Christensen
Date: 2014-11-15 00:00:00

A fundamental challenge to sensory processing tasks in perception and
robotics is the problem of obtaining data associations across views. We present
a robust solution for ascertaining potentially dense surface patch (superpixel)
associations, requiring just range information. Our approach involves
decomposition of a view into regularized surface patches. We represent them as
sequences expressing geometry invariantly over their superpixel neighborhoods,
as uniquely consistent partial orderings. We match these representations
through an optimal sequence comparison metric based on the Damerau-Levenshtein
distance - enabling robust association with quadratic complexity (in contrast
to hitherto employed joint matching formulations which are NP-complete). The
approach is able to perform under wide baselines, heavy rotations, partial
overlaps, significant occlusions and sensor noise.
  The technique does not require any priors -- motion or otherwise, and does
not make restrictive assumptions on scene structure and sensor movement. It
does not require appearance -- is hence more widely applicable than appearance
reliant methods, and invulnerable to related ambiguities such as textureless or
aliased content. We present promising qualitative and quantitative results
under diverse settings, along with comparatives with popular approaches based
on range as well as RGB-D data.
Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing
Authors: Hongwei Li, Bin Yu
Date: 2014-11-15 00:00:00

Crowdsourcing has become an effective and popular tool for human-powered
computation to label large datasets. Since the workers can be unreliable, it is
common in crowdsourcing to assign multiple workers to one task, and to
aggregate the labels in order to obtain results of high quality. In this paper,
we provide finite-sample exponential bounds on the error rate (in probability
and in expectation) of general aggregation rules under the Dawid-Skene
crowdsourcing model. The bounds are derived for multi-class labeling, and can
be used to analyze many aggregation methods, including majority voting,
weighted majority voting and the oracle Maximum A Posteriori (MAP) rule. We
show that the oracle MAP rule approximately optimizes our upper bound on the
mean error rate of weighted majority voting in certain setting. We propose an
iterative weighted majority voting (IWMV) method that optimizes the error rate
bound and approximates the oracle MAP rule. Its one step version has a provable
theoretical guarantee on the error rate. The IWMV method is intuitive and
computationally simple. Experimental results on simulated and real data show
that IWMV performs at least on par with the state-of-the-art methods, and it
has a much lower computational cost (around one hundred times faster) than the
state-of-the-art methods.
6 Seconds of Sound and Vision: Creativity in Micro-Videos
Authors: Miriam Redi, Neil O Hare, Rossano Schifanella, Michele Trevisiol, Alejandro Jaimes
Date: 2014-11-14 00:00:00
Proceeding: CVPR 2014 6

The notion of creativity, as opposed to related concepts such as beauty or
interestingness, has not been studied from the perspective of automatic
analysis of multimedia content. Meanwhile, short online videos shared on social
media platforms, or micro-videos, have arisen as a new medium for creative
expression. In this paper we study creative micro-videos in an effort to
understand the features that make a video creative, and to address the problem
of automatic detection of creative content. Defining creative videos as those
that are novel and have aesthetic value, we conduct a crowdsourcing experiment
to create a dataset of over 3,800 micro-videos labelled as creative and
non-creative. We propose a set of computational features that we map to the
components of our definition of creativity, and conduct an analysis to
determine which of these features correlate most with creative video. Finally,
we evaluate a supervised approach to automatically detect creative video, with
promising results, showing that it is necessary to model both aesthetic value
and novelty to achieve optimal classification accuracy.
A framework for studying synaptic plasticity with neural spike train data
Authors: Scott W. Linderman, Christopher H. Stock, Ryan P. Adams
Date: 2014-11-14 00:00:00
Proceeding: NeurIPS 2014 12

Learning and memory in the brain are implemented by complex, time-varying
changes in neural circuitry. The computational rules according to which
synaptic weights change over time are the subject of much research, and are not
precisely understood. Until recently, limitations in experimental methods have
made it challenging to test hypotheses about synaptic plasticity on a large
scale. However, as such data become available and these barriers are lifted, it
becomes necessary to develop analysis techniques to validate plasticity models.
Here, we present a highly extensible framework for modeling arbitrary synaptic
plasticity rules on spike train data in populations of interconnected neurons.
We treat synaptic weights as a (potentially nonlinear) dynamical system
embedded in a fully-Bayesian generalized linear model (GLM). In addition, we
provide an algorithm for inferring synaptic weight trajectories alongside the
parameters of the GLM and of the learning rules. Using this method, we perform
model comparison of two proposed variants of the well-known
spike-timing-dependent plasticity (STDP) rule, where nonlinear effects play a
substantial role. On synthetic data generated from the biophysical simulator
NEURON, we show that we can recover the weight trajectories, the pattern of
connectivity, and the underlying learning rules.
Association Rule Based Flexible Machine Learning Module for Embedded System Platforms like Android
Authors: Amiraj Dhawan, Shruti Bhave, Amrita Aurora, Vishwanathan Iyer
Date: 2014-11-14 00:00:00
Tasks: BIG-bench Machine Learning

The past few years have seen a tremendous growth in the popularity of
smartphones. As newer features continue to be added to smartphones to increase
their utility, their significance will only increase in future. Combining
machine learning with mobile computing can enable smartphones to become
'intelligent' devices, a feature which is hitherto unseen in them. Also, the
combination of machine learning and context aware computing can enable
smartphones to gauge user's requirements proactively, depending upon their
environment and context. Accordingly, necessary services can be provided to
users.
  In this paper, we have explored the methods and applications of integrating
machine learning and context aware computing on the Android platform, to
provide higher utility to the users. To achieve this, we define a Machine
Learning (ML) module which is incorporated in the basic Android architecture.
Firstly, we have outlined two major functionalities that the ML module should
provide. Then, we have presented three architectures, each of which
incorporates the ML module at a different level in the Android architecture.
The advantages and shortcomings of each of these architectures have been
evaluated. Lastly, we have explained a few applications in which our proposed
system can be incorporated such that their functionality is improved.
Learning Multi-Relational Semantics Using Neural-Embedding Models
Authors: Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng
Date: 2014-11-14 00:00:00
Tasks: Knowledge Base Completion

In this paper we present a unified framework for modeling multi-relational
representations, scoring, and learning, and conduct an empirical study of
several recent multi-relational embedding models under the framework. We
investigate the different choices of relation operators based on linear and
bilinear transformations, and also the effects of entity representations by
incorporating unsupervised vectors pre-trained on extra textual resources. Our
results show several interesting findings, enabling the design of a simple
embedding model that achieves the new state-of-the-art performance on a popular
knowledge base completion task evaluated on Freebase.
A unified view of generative models for networks: models, methods, opportunities, and challenges
Authors: Abigail Z. Jacobs, Aaron Clauset
Date: 2014-11-14 00:00:00
Tasks: BIG-bench Machine Learning, Diversity, Sociology

Research on probabilistic models of networks now spans a wide variety of
fields, including physics, sociology, biology, statistics, and machine
learning. These efforts have produced a diverse ecology of models and methods.
Despite this diversity, many of these models share a common underlying
structure: pairwise interactions (edges) are generated with probability
conditional on latent vertex attributes. Differences between models generally
stem from different philosophical choices about how to learn from data or
different empirically-motivated goals. The highly interdisciplinary nature of
work on these generative models, however, has inhibited the development of a
unified view of their similarities and differences. For instance, novel
theoretical models and optimization techniques developed in machine learning
are largely unknown within the social and biological sciences, which have
instead emphasized model interpretability. Here, we describe a unified view of
generative models for networks that draws together many of these disparate
threads and highlights the fundamental similarities and differences that span
these fields. We then describe a number of opportunities and challenges for
future work that are revealed by this view.
Dynamic Programming for Instance Annotation in Multi-instance Multi-label Learning
Authors: Anh T. Pham, Raviv Raich, Xiaoli Z. Fern
Date: 2014-11-14 00:00:00
Tasks: Activity Recognition, Multi-Label Learning

Labeling data for classification requires significant human effort. To reduce
labeling cost, instead of labeling every instance, a group of instances (bag)
is labeled by a single bag label. Computer algorithms are then used to infer
the label for each instance in a bag, a process referred to as instance
annotation. This task is challenging due to the ambiguity regarding the
instance labels. We propose a discriminative probabilistic model for the
instance annotation problem and introduce an expectation maximization framework
for inference, based on the maximum likelihood approach. For many probabilistic
approaches, brute-force computation of the instance label posterior probability
given its bag label is exponential in the number of instances in the bag. Our
key contribution is a dynamic programming method for computing the posterior
that is linear in the number of instances. We evaluate our methods using both
benchmark and real world data sets, in the domain of bird song, image
annotation, and activity recognition. In many cases, the proposed framework
outperforms, sometimes significantly, the current state-of-the-art MIML
learning methods, both in instance label prediction and bag label prediction.
A Faster Method for Tracking and Scoring Videos Corresponding to Sentences
Authors: Haonan Yu, Daniel P. Barrett, Jeffrey Mark Siskind
Date: 2014-11-14 00:00:00
Tasks: Retrieval, Sentence, Video Retrieval

Prior work presented the sentence tracker, a method for scoring how well a
sentence describes a video clip or alternatively how well a video clip depicts
a sentence. We present an improved method for optimizing the same cost function
employed by this prior work, reducing the space complexity from exponential in
the sentence length to polynomial, as well as producing a qualitatively
identical result in time polynomial in the sentence length instead of
exponential. Since this new method is plug-compatible with the prior method, it
can be used for the same applications: video retrieval with sentential queries,
generating sentential descriptions of video clips, and focusing the attention
of a tracker with a sentence, while allowing these applications to scale with
significantly larger numbers of object detections, word meanings modeled with
HMMs with significantly larger numbers of states, and significantly longer
sentences, with no appreciable degradation in quality of results.
Automatic Generation of Alternative Starting Positions for Simple Traditional Board Games
Authors: Umair Z. Ahmed, Krishnendu Chatterjee, Sumit Gulwani
Date: 2014-11-14 00:00:00
Tasks: Board Games

Simple board games, like Tic-Tac-Toe and CONNECT-4, play an important role
not only in the development of mathematical and logical skills, but also in the
emotional and social development. In this paper, we address the problem of
generating targeted starting positions for such games. This can facilitate new
approaches for bringing novice players to mastery, and also leads to discovery
of interesting game variants. We present an approach that generates starting
states of varying hardness levels for player~$1$ in a two-player board game,
given rules of the board game, the desired number of steps required for
player~$1$ to win, and the expertise levels of the two players. Our approach
leverages symbolic methods and iterative simulation to efficiently search the
extremely large state space. We present experimental results that include
discovery of states of varying hardness levels for several simple grid-based
board games. The presence of such states for standard game variants like $4
\times 4$ Tic-Tac-Toe opens up new games to be played that have never been
played as the default start state is heavily biased.
Detecting change points in the large-scale structure of evolving networks
Authors: Leto Peel, Aaron Clauset
Date: 2014-03-05 00:00:00
Tasks: Change Point Detection

Interactions among people or objects are often dynamic in nature and can be
represented as a sequence of networks, each providing a snapshot of the
interactions over a brief period of time. An important task in analyzing such
evolving networks is change-point detection, in which we both identify the
times at which the large-scale pattern of interactions changes fundamentally
and quantify how large and what kind of change occurred. Here, we formalize for
the first time the network change-point detection problem within an online
probabilistic learning framework and introduce a method that can reliably solve
it. This method combines a generalized hierarchical random graph model with a
Bayesian hypothesis test to quantitatively determine if, when, and precisely
how a change point has occurred. We analyze the detectability of our method
using synthetic data with known change points of different types and
magnitudes, and show that this method is more accurate than several previously
used alternatives. Applied to two high-resolution evolving social networks,
this method identifies a sequence of change points that align with known
external "shocks" to these networks.
A Discriminative CNN Video Representation for Event Detection
Authors: Zhongwen Xu, Yi Yang, Alexander G. Hauptmann
Date: 2014-11-14 00:00:00
Proceeding: CVPR 2015 6
Tasks: Event Detection

In this paper, we propose a discriminative video representation for event
detection over a large scale video dataset when only limited hardware resources
are available. The focus of this paper is to effectively leverage deep
Convolutional Neural Networks (CNNs) to advance event detection, where only
frame level static descriptors can be extracted by the existing CNN toolkit.
This paper makes two contributions to the inference of CNN video
representation. First, while average pooling and max pooling have long been the
standard approaches to aggregating frame level static features, we show that
performance can be significantly improved by taking advantage of an appropriate
encoding method. Second, we propose using a set of latent concept descriptors
as the frame descriptor, which enriches visual information while keeping it
computationally affordable. The integration of the two contributions results in
a new state-of-the-art performance in event detection over the largest video
datasets. Compared to improved Dense Trajectories, which has been recognized as
the best video representation for event detection, our new representation
improves the Mean Average Precision (mAP) from 27.6% to 36.8% for the TRECVID
MEDTest 14 dataset and from 34.0% to 44.6% for the TRECVID MEDTest 13 dataset.
This work is the core part of the winning solution of our CMU-Informedia team
in TRECVID MED 2014 competition.
A convex formulation for hyperspectral image superresolution via subspace-based regularization
Authors: Miguel Simões, José Bioucas-Dias, Luis B. Almeida, Jocelyn Chanussot
Date: 2014-11-14 00:00:00

Hyperspectral remote sensing images (HSIs) usually have high spectral
resolution and low spatial resolution. Conversely, multispectral images (MSIs)
usually have low spectral and high spatial resolutions. The problem of
inferring images which combine the high spectral and high spatial resolutions
of HSIs and MSIs, respectively, is a data fusion problem that has been the
focus of recent active research due to the increasing availability of HSIs and
MSIs retrieved from the same geographical area.
  We formulate this problem as the minimization of a convex objective function
containing two quadratic data-fitting terms and an edge-preserving regularizer.
The data-fitting terms account for blur, different resolutions, and additive
noise. The regularizer, a form of vector Total Variation, promotes
piecewise-smooth solutions with discontinuities aligned across the
hyperspectral bands.
  The downsampling operator accounting for the different spatial resolutions,
the non-quadratic and non-smooth nature of the regularizer, and the very large
size of the HSI to be estimated lead to a hard optimization problem. We deal
with these difficulties by exploiting the fact that HSIs generally "live" in a
low-dimensional subspace and by tailoring the Split Augmented Lagrangian
Shrinkage Algorithm (SALSA), which is an instance of the Alternating Direction
Method of Multipliers (ADMM), to this optimization problem, by means of a
convenient variable splitting. The spatial blur and the spectral linear
operators linked, respectively, with the HSI and MSI acquisition processes are
also estimated, and we obtain an effective algorithm that outperforms the
state-of-the-art, as illustrated in a series of experiments with simulated and
real-life data.