pwc-archive/papers-with-abstracts huggingface dataset:
Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions
Authors: Arno Solin, Pasi Jylänki, Jaakko Kauramäki, Tom Heskes, Marcel A. J. van Gerven, Simo Särkkä
Date: 2016-04-17 00:00:00

In magnetoencephalography (MEG) the conventional approach to source
reconstruction is to solve the underdetermined inverse problem independently
over time and space. Here we present how the conventional approach can be
extended by regularizing the solution in space and time by a Gaussian process
(Gaussian random field) model. Assuming a separable covariance function in
space and time, the computational complexity of the proposed model becomes
(without any further assumptions or restrictions) $\mathcal{O}(t^3 + n^3 +
m^2n)$, where $t$ is the number of time steps, $m$ is the number of sources,
and $n$ is the number of sensors. We apply the method to both simulated and
empirical data, and demonstrate the efficiency and generality of our Bayesian
source reconstruction approach which subsumes various classical approaches in
the literature.
Learning to Incentivize: Eliciting Effort via Output Agreement
Authors: Yang Liu, Yi-Ling Chen
Date: 2016-04-17 00:00:00

In crowdsourcing when there is a lack of verification for contributed
answers, output agreement mechanisms are often used to incentivize participants
to provide truthful answers when the correct answer is hold by the majority. In
this paper, we focus on using output agreement mechanisms to elicit effort, in
addition to eliciting truthful answers, from a population of workers. We
consider a setting where workers have heterogeneous cost of effort exertion and
examine the data requester's problem of deciding the reward level in output
agreement for optimal elicitation. In particular, when the requester knows the
cost distribution, we derive the optimal reward level for output agreement
mechanisms. This is achieved by first characterizing Bayesian Nash equilibria
of output agreement mechanisms for a given reward level. When the requester
does not know the cost distribution, we develop sequential mechanisms that
combine learning the cost distribution with incentivizing effort exertion to
approximately determine the optimal reward level.
Some medical applications of example-based super-resolution
Authors: Ramin Zabih
Date: 2016-04-17 00:00:00
Tasks: Super-Resolution

Example-based super-resolution (EBSR) reconstructs a high-resolution image
from a low-resolution image, given a training set of high-resolution images. In
this note I propose some applications of EBSR to medical imaging. A particular
interesting application, which I call "x-ray voxelization", approximates the
result of a CT scan from an x-ray image.
IFP-Intuitionistic fuzzy soft set theory and its applications
Authors: Faruk Karaaslan, Naim Cagman, Saban Yilmaz
Date: 2013-04-02 00:00:00
Tasks: Decision Making, Form

In this work, we present definition of intuitionistic fuzzy parameterized
(IFP) intuitionistic fuzzy soft set and its operations. Then we define
IFP-aggregation operator to form IFP-intuitionistic fuzzy soft-decision-making
method which allows constructing more efficient decision processes.
Generating Semi-Synthetic Validation Benchmarks for Embryomics
Authors: Johannes Stegmaier, Julian Arz, Benjamin Schott, Jens C. Otte, Andrei Kobitski, G. Ulrich Nienhaus, Uwe Strähle, Peter Sanders, Ralf Mikut
Date: 2016-04-17 00:00:00

Systematic validation is an essential part of algorithm development. The
enormous dataset sizes and the complexity observed in many recent time-resolved
3D fluorescence microscopy imaging experiments, however, prohibit a
comprehensive manual ground truth generation. Moreover, existing simulated
benchmarks in this field are often too simple or too specialized to
sufficiently validate the observed image analysis problems. We present a new
semi-synthetic approach to generate realistic 3D+t benchmarks that combines
challenging cellular movement dynamics of real embryos with simulated
fluorescent nuclei and artificial image distortions including various
parametrizable options like cell numbers, acquisition deficiencies or multiview
simulations. We successfully applied the approach to simulate the development
of a zebrafish embryo with thousands of cells over 14 hours of its early
existence.
From Denoising to Compressed Sensing
Authors: Christopher A. Metzler, Arian Maleki, Richard G. Baraniuk
Date: 2014-06-16 00:00:00
Tasks: compressed sensing, Denoising

A denoising algorithm seeks to remove noise, errors, or perturbations from a
signal. Extensive research has been devoted to this arena over the last several
decades, and as a result, today's denoisers can effectively remove large
amounts of additive white Gaussian noise. A compressed sensing (CS)
reconstruction algorithm seeks to recover a structured signal acquired using a
small number of randomized measurements. Typical CS reconstruction algorithms
can be cast as iteratively estimating a signal from a perturbed observation.
This paper answers a natural question: How can one effectively employ a generic
denoiser in a CS reconstruction algorithm? In response, we develop an extension
of the approximate message passing (AMP) framework, called Denoising-based AMP
(D-AMP), that can integrate a wide class of denoisers within its iterations. We
demonstrate that, when used with a high performance denoiser for natural
images, D-AMP offers state-of-the-art CS recovery performance while operating
tens of times faster than competing methods. We explain the exceptional
performance of D-AMP by analyzing some of its theoretical features. A key
element in D-AMP is the use of an appropriate Onsager correction term in its
iterations, which coerces the signal perturbation at each iteration to be very
close to the white Gaussian noise that denoisers are typically designed to
remove.
A global constraint for closed itemset mining
Authors: Mehdi Maamar, Nadjib Lazaar, Samir Loudni, Yahia Lebbah
Date: 2016-04-17 00:00:00

Discovering the set of closed frequent patterns is one of the fundamental
problems in Data Mining. Recent Constraint Programming (CP) approaches for
declarative itemset mining have proven their usefulness and flexibility. But
the wide use of reified constraints in current CP approaches raises many
difficulties to cope with high dimensional datasets. This paper proposes CLOSED
PATTERN global constraint which does not require any reified constraints nor
any extra variables to encode efficiently the Closed Frequent Pattern Mining
(CFPM) constraint. CLOSED-PATTERN captures the particular semantics of the CFPM
problem in order to ensure a polynomial pruning algorithm ensuring domain
consistency. The computational properties of our constraint are analyzed and
their practical effectiveness is experimentally evaluated.
An Initial Seed Selection Algorithm for K-means Clustering of Georeferenced Data to Improve Replicability of Cluster Assignments for Mapping Application
Authors: Fouad Khan
Date: 2016-04-17 00:00:00
Tasks: Attribute, Clustering, Computational Efficiency

K-means is one of the most widely used clustering algorithms in various
disciplines, especially for large datasets. However the method is known to be
highly sensitive to initial seed selection of cluster centers. K-means++ has
been proposed to overcome this problem and has been shown to have better
accuracy and computational efficiency than k-means. In many clustering problems
though -such as when classifying georeferenced data for mapping applications-
standardization of clustering methodology, specifically, the ability to arrive
at the same cluster assignment for every run of the method i.e. replicability
of the methodology, may be of greater significance than any perceived measure
of accuracy, especially when the solution is known to be non-unique, as in the
case of k-means clustering. Here we propose a simple initial seed selection
algorithm for k-means clustering along one attribute that draws initial cluster
boundaries along the 'deepest valleys' or greatest gaps in dataset. Thus, it
incorporates a measure to maximize distance between consecutive cluster centers
which augments the conventional k-means optimization for minimum distance
between cluster center and cluster members. Unlike existing initialization
methods, no additional parameters or degrees of freedom are introduced to the
clustering algorithm. This improves the replicability of cluster assignments by
as much as 100% over k-means and k-means++, virtually reducing the variance
over different runs to zero, without introducing any additional parameters to
the clustering process. Further, the proposed method is more computationally
efficient than k-means++ and in some cases, more accurate.
Mahalanobis Distance Metric Learning Algorithm for Instance-based Data Stream Classification
Authors: Jorge Luis Rivero Perez, Bernardete Ribeiro, Carlos Morell Perez
Date: 2016-04-17 00:00:00
Tasks: Classification, Drift Detection, General Classification, Metric Learning

With the massive data challenges nowadays and the rapid growing of
technology, stream mining has recently received considerable attention. To
address the large number of scenarios in which this phenomenon manifests itself
suitable tools are required in various research fields. Instance-based data
stream algorithms generally employ the Euclidean distance for the
classification task underlying this problem. A novel way to look into this
issue is to take advantage of a more flexible metric due to the increased
requirements imposed by the data stream scenario. In this paper we present a
new algorithm that learns a Mahalanobis metric using similarity and
dissimilarity constraints in an online manner. This approach hybridizes a
Mahalanobis distance metric learning algorithm and a k-NN data stream
classification algorithm with concept drift detection. First, some basic
aspects of Mahalanobis distance metric learning are described taking into
account key properties as well as online distance metric learning algorithms.
Second, we implement specific evaluation methodologies and comparative metrics
such as Q statistic for data stream classification algorithms. Finally, our
algorithm is evaluated on different datasets by comparing its results with one
of the best instance-based data stream classification algorithm of the state of
the art. The results demonstrate that our proposal is better
From Incremental Meaning to Semantic Unit (phrase by phrase)
Authors: Andreas Scherbakov, Ekaterina Vylomova, Fei Liu, Timothy Baldwin
Date: 2016-04-17 00:00:00
Tasks: Word Embeddings

This paper describes an experimental approach to Detection of Minimal
Semantic Units and their Meaning (DiMSUM), explored within the framework of
SemEval 2016 Task 10. The approach is primarily based on a combination of word
embeddings and parserbased features, and employs unidirectional incremental
computation of compositional embeddings for multiword expressions.
Subjects and Their Objects: Localizing Interactees for a Person-Centric View of Importance
Authors: Chao-Yeh Chen, Kristen Grauman
Date: 2016-04-17 00:00:00
Tasks: Image Retargeting, Object, object-detection, Object Detection, Position

Understanding images with people often entails understanding their
\emph{interactions} with other objects or people. As such, given a novel image,
a vision system ought to infer which other objects/people play an important
role in a given person's activity. However, existing methods are limited to
learning action-specific interactions (e.g., how the pose of a tennis player
relates to the position of his racquet when serving the ball) for improved
recognition, making them unequipped to reason about novel interactions with
actions or objects unobserved in the training data.
  We propose to predict the "interactee" in novel images---that is, to localize
the \emph{object} of a person's action. Given an arbitrary image with a
detected person, the goal is to produce a saliency map indicating the most
likely positions and scales where that person's interactee would be found. To
that end, we explore ways to learn the generic, action-independent connections
between (a) representations of a person's pose, gaze, and scene cues and (b)
the interactee object's position and scale. We provide results on a newly
collected UT Interactee dataset spanning more than 10,000 images from SUN,
PASCAL, and COCO. We show that the proposed interaction-informed saliency
metric has practical utility for four tasks: contextual object detection, image
retargeting, predicting object importance, and data-driven natural language
scene description. All four scenarios reveal the value in linking the subject
to its object in order to understand the story of an image.
Probabilistic Receiver Architecture Combining BP, MF, and EP for Multi-Signal Detection
Authors: Daniel J. Jakubisin, R. Michael Buehrer, Claudio R. C. M. da Silva
Date: 2016-04-17 00:00:00
Tasks: graph construction

Receiver algorithms which combine belief propagation (BP) with the mean field
(MF) approximation are well-suited for inference of both continuous and
discrete random variables. In wireless scenarios involving detection of
multiple signals, the standard construction of the combined BP-MF framework
includes the equalization or multi-user detection functions within the MF
subgraph. In this paper, we show that the MF approximation is not particularly
effective for multi-signal detection. We develop a new factor graph
construction for application of the BP-MF framework to problems involving the
detection of multiple signals. We then develop a low-complexity variant to the
proposed construction in which Gaussian BP is applied to the equalization
factors. In this case, the factor graph of the joint probability distribution
is divided into three subgraphs: (i) a MF subgraph comprised of the observation
factors and channel estimation, (ii) a Gaussian BP subgraph which is applied to
multi-signal detection, and (iii) a discrete BP subgraph which is applied to
demodulation and decoding. Expectation propagation is used to approximate
discrete distributions with a Gaussian distribution and links the discrete BP
and Gaussian BP subgraphs. The result is a probabilistic receiver architecture
with strong theoretical justification which can be applied to multi-signal
detection.
Visual saliency detection: a Kalman filter based approach
Authors: Sourya Roy, Pabitra Mitra
Date: 2016-04-17 00:00:00
Tasks: Saliency Detection, Saliency Prediction

In this paper we propose a Kalman filter aided saliency detection model which
is based on the conjecture that salient regions are considerably different from
our "visual expectation" or they are "visually surprising" in nature. In this
work, we have structured our model with an immediate objective to predict
saliency in static images. However, the proposed model can be easily extended
for space-time saliency prediction. Our approach was evaluated using two
publicly available benchmark data sets and results have been compared with
other existing saliency models. The results clearly illustrate the superior
performance of the proposed model over other approaches.
Supervised and Unsupervised Ensembling for Knowledge Base Population
Authors: Nazneen Fatema Rajani, Raymond J. Mooney
Date: 2016-04-16 00:00:00
Tasks: Knowledge Base Population, slot-filling, Slot Filling

We present results on combining supervised and unsupervised methods to
ensemble multiple systems for two popular Knowledge Base Population (KBP)
tasks, Cold Start Slot Filling (CSSF) and Tri-lingual Entity Discovery and
Linking (TEDL). We demonstrate that our combined system along with auxiliary
features outperforms the best performing system for both tasks in the 2015
competition, several ensembling baselines, as well as the state-of-the-art
stacking approach to ensembling KBP systems. The success of our technique on
two different and challenging problems demonstrates the power and generality of
our combined approach to ensembling.
Variance Reduction in SGD by Distributed Importance Sampling
Authors: Guillaume Alain, Alex Lamb, Chinnadhurai Sankar, Aaron Courville, Yoshua Bengio
Date: 2015-11-20 00:00:00

Humans are able to accelerate their learning by selecting training materials
that are the most informative and at the appropriate level of difficulty. We
propose a framework for distributing deep learning in which one set of workers
search for the most informative examples in parallel while a single worker
updates the model on examples selected by importance sampling. This leads the
model to update using an unbiased estimate of the gradient which also has
minimum variance when the sampling proposal is proportional to the L2-norm of
the gradient. We show experimentally that this method reduces gradient variance
even in a context where the cost of synchronization across machines cannot be
ignored, and where the factors for importance sampling are not updated
instantly across the training set.
ACD: Action Concept Discovery from Image-Sentence Corpora
Authors: Jiyang Gao, Chen Sun, Ram Nevatia
Date: 2016-04-16 00:00:00
Tasks: Action Classification, Classification, General Classification, Sentence

Action classification in still images is an important task in computer
vision. It is challenging as the appearances of ac- tions may vary depending on
their context (e.g. associated objects). Manually labeling of context
information would be time consuming and difficult to scale up. To address this
challenge, we propose a method to automatically discover and cluster action
concepts, and learn their classifiers from weakly supervised image-sentence
corpora. It obtains candidate action concepts by extracting verb-object pairs
from sentences and verifies their visualness with the associated images.
Candidate action concepts are then clustered by using a multi-modal
representation with image embeddings from deep convolutional networks and text
embeddings from word2vec. More than one hundred human action concept
classifiers are learned from the Flickr 30k dataset with no additional human
effort and promising classification results are obtained. We further apply the
AdaBoost algorithm to automatically select and combine relevant action concepts
given an action query. Promising results have been shown on the PASCAL VOC 2012
action classification benchmark, which has zero overlap with Flickr30k.
Efficient Dictionary Learning with Sparseness-Enforcing Projections
Authors: Markus Thom, Matthias Rapp, Günther Palm
Date: 2016-04-16 00:00:00
Tasks: Denoising, Dictionary Learning

Learning dictionaries suitable for sparse coding instead of using engineered
bases has proven effective in a variety of image processing tasks. This paper
studies the optimization of dictionaries on image data where the representation
is enforced to be explicitly sparse with respect to a smooth, normalized
sparseness measure. This involves the computation of Euclidean projections onto
level sets of the sparseness measure. While previous algorithms for this
optimization problem had at least quasi-linear time complexity, here the first
algorithm with linear time complexity and constant space complexity is
proposed. The key for this is the mathematically rigorous derivation of a
characterization of the projection's result based on a soft-shrinkage function.
This theory is applied in an original algorithm called Easy Dictionary Learning
(EZDL), which learns dictionaries with a simple and fast-to-compute
Hebbian-like learning rule. The new algorithm is efficient, expressive and
particularly simple to implement. It is demonstrated that despite its
simplicity, the proposed learning algorithm is able to generate a rich variety
of dictionaries, in particular a topographic organization of atoms or separable
atoms. Further, the dictionaries are as expressive as those of benchmark
learning algorithms in terms of the reproduction quality on entire images, and
result in an equivalent denoising performance. EZDL learns approximately 30 %
faster than the already very efficient Online Dictionary Learning algorithm,
and is therefore eligible for rapid data set analysis and problems with vast
quantities of learning samples.
Closed loop interactions between spiking neural network and robotic simulators based on MUSIC and ROS
Authors: Philipp Weidel, Mikael Djurfeldt, Renato Duarte, Abigail Morrison
Date: 2016-04-16 00:00:00
Tasks: Reinforcement Learning

In order to properly assess the function and computational properties of
simulated neural systems, it is necessary to account for the nature of the
stimuli that drive the system. However, providing stimuli that are rich and yet
both reproducible and amenable to experimental manipulations is technically
challenging, and even more so if a closed-loop scenario is required. In this
work, we present a novel approach to solve this problem, connecting robotics
and neural network simulators. We implement a middleware solution that bridges
the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC).
This enables any robotic and neural simulators that implement the corresponding
interfaces to be efficiently coupled, allowing real-time performance for a wide
range of configurations. This work extends the toolset available for
researchers in both neurorobotics and computational neuroscience, and creates
the opportunity to perform closed-loop experiments of arbitrary complexity to
address questions in multiple areas, including embodiment, agency, and
reinforcement learning.
Multi-Oriented Text Detection with Fully Convolutional Networks
Authors: Zheng Zhang, Chengquan Zhang, Wei Shen, Cong Yao, Wenyu Liu, Xiang Bai
Date: 2016-04-14 00:00:00
Proceeding: CVPR 2016 6
Tasks: Scene Text Detection, Text Detection

In this paper, we propose a novel approach for text detec- tion in natural
images. Both local and global cues are taken into account for localizing text
lines in a coarse-to-fine pro- cedure. First, a Fully Convolutional Network
(FCN) model is trained to predict the salient map of text regions in a holistic
manner. Then, text line hypotheses are estimated by combining the salient map
and character components. Fi- nally, another FCN classifier is used to predict
the centroid of each character, in order to remove the false hypotheses. The
framework is general for handling text in multiple ori- entations, languages
and fonts. The proposed method con- sistently achieves the state-of-the-art
performance on three text detection benchmarks: MSRA-TD500, ICDAR2015 and
ICDAR2013.
Smoothed Hierarchical Dirichlet Process: A Non-Parametric Approach to Constraint Measures
Authors: Cheng Luo, Yang Xiang, Richard Yi Da Xu
Date: 2016-04-16 00:00:00

Time-varying mixture densities occur in many scenarios, for example, the
distributions of keywords that appear in publications may evolve from year to
year, video frame features associated with multiple targets may evolve in a
sequence. Any models that realistically cater to this phenomenon must exhibit
two important properties: the underlying mixture densities must have an unknown
number of mixtures, and there must be some "smoothness" constraints in place
for the adjacent mixture densities. The traditional Hierarchical Dirichlet
Process (HDP) may be suited to the first property, but certainly not the
second. This is due to how each random measure in the lower hierarchies is
sampled independent of each other and hence does not facilitate any temporal
correlations. To overcome such shortcomings, we proposed a new Smoothed
Hierarchical Dirichlet Process (sHDP). The key novelty of this model is that we
place a temporal constraint amongst the nearby discrete measures $\{G_j\}$ in
the form of symmetric Kullback-Leibler (KL) Divergence with a fixed bound $B$.
Although the constraint we place only involves a single scalar value, it
nonetheless allows for flexibility in the corresponding successive measures.
Remarkably, it also led us to infer the model within the stick-breaking process
where the traditional Beta distribution used in stick-breaking is now replaced
by a new constraint calculated from $B$. We present the inference algorithm and
elaborate on its solutions. Our experiment using NIPS keywords has shown the
desirable effect of the model.