pwc-archive/papers-with-abstracts huggingface dataset:
Identification and Counting White Blood Cells and Red Blood Cells using Image Processing Case Study of Leukemia
Authors: Esti Suryani, Wiharto Wiharto, Nizomjon Polvonov
Date: 2015-11-16 00:00:00
Tasks: All, Diagnostic, Edge Detection

Leukemia is diagnosed with complete blood counts which is by calculating all
blood cells and compare the number of white blood cells (White Blood Cells /
WBC) and red blood cells (Red Blood Cells / RBC). Information obtained from a
complete blood count, has become a cornerstone in the hematology laboratory for
diagnostic purposes and monitoring of hematological disorders. However, the
traditional procedure for counting blood cells manually requires effort and a
long time, therefore this method is one of the most expensive routine tests in
laboratory hematology clinic. Solution for such kind of time consuming task and
necessity of data tracability can be found in image processing techniques based
on blood cell morphology . This study aims to identify Acute Lymphocytic
Leukemia (ALL) and Acute Myeloid Leukemia type M3 (AML M3) using Fuzzy Rule
Based System based on morphology of white blood cells. Characteristic
parameters witch extractedare WBC Area, Nucleus and Granule Ratio of white
blood cells. Image processing algorithms such as thresholding, Canny edge
detection and color identification filters are used.Then for identification of
ALL, AML M3 and Healthy cells used Fuzzy Rule Based System with Sugeno method.
In the testing process used 104 images out of which 29 ALL - Positive, 50 AML
M3 - Positive and 25 Healthy cells. Test results showed 83.65 % accuracy .
Graph-based denoising for time-varying point clouds
Authors: Yann Schoenenberger, Johan Paratte, Pierre Vandergheynst
Date: 2015-11-16 00:00:00
Tasks: Denoising, Time Series, Time Series Analysis

Noisy 3D point clouds arise in many applications. They may be due to errors
when constructing a 3D model from images or simply to imprecise depth sensors.
Point clouds can be given geometrical structure using graphs created from the
similarity information between points. This paper introduces a technique that
uses this graph structure and convex optimization methods to denoise 3D point
clouds. A short discussion presents how those methods naturally generalize to
time-varying inputs such as 3D point cloud time series.
Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression
Authors: Zhiao Huang, Erjin Zhou, Zhimin Cao
Date: 2015-11-16 00:00:00
Tasks: Face Alignment, Face Recognition, Position, regression

Facial landmark localization plays an important role in face recognition and
analysis applications. In this paper, we give a brief introduction to a
coarse-to-fine pipeline with neural networks and sequential regression. First,
a global convolutional network is applied to the holistic facial image to give
an initial landmark prediction. A pyramid of multi-scale local image patches is
then cropped to feed to a new network for each landmark to refine the
prediction. As the refinement network outputs a more accurate position
estimation than the input, such procedure could be repeated several times until
the estimation converges. We evaluate our system on the 300-W dataset [11] and
it outperforms the recent state-of-the-arts.
Fast clustering for scalable statistical analysis on structured images
Authors: Bertrand Thirion, Andrés Hoyos-Idrobo, Jonas Kahn, Gael Varoquaux
Date: 2015-11-16 00:00:00
Tasks: Clustering, Computational Efficiency, Dimensionality Reduction

The use of brain images as markers for diseases or behavioral differences is
challenged by the small effects size and the ensuing lack of power, an issue
that has incited researchers to rely more systematically on large cohorts.
Coupled with resolution increases, this leads to very large datasets. A
striking example in the case of brain imaging is that of the Human Connectome
Project: 20 Terabytes of data and growing. The resulting data deluge poses
severe challenges regarding the tractability of some processing steps
(discriminant analysis, multivariate models) due to the memory demands posed by
these data. In this work, we revisit dimension reduction approaches, such as
random projections, with the aim of replacing costly function evaluations by
cheaper ones while decreasing the memory requirements. Specifically, we
investigate the use of alternate schemes, based on fast clustering, that are
well suited for signals exhibiting a strong spatial structure, such as
anatomical and functional brain images. Our contribution is twofold: i) we
propose a linear-time clustering scheme that bypasses the percolation issues
inherent in these algorithms and thus provides compressions nearly as good as
traditional quadratic-complexity variance-minimizing clustering schemes, ii) we
show that cluster-based compression can have the virtuous effect of removing
high-frequency noise, actually improving subsequent estimations steps. As a
consequence, the proposed approach yields very accurate models on several
large-scale problems yet with impressive gains in computational efficiency,
making it possible to analyze large datasets.
Active Contextual Entropy Search
Authors: Jan Hendrik Metzen
Date: 2015-11-13 00:00:00
Tasks: Bayesian Optimization

Contextual policy search allows adapting robotic movement primitives to
different situations. For instance, a locomotion primitive might be adapted to
different terrain inclinations or desired walking speeds. Such an adaptation is
often achievable by modifying a small number of hyperparameters. However,
learning, when performed on real robotic systems, is typically restricted to a
small number of trials. Bayesian optimization has recently been proposed as a
sample-efficient means for contextual policy search that is well suited under
these conditions. In this work, we extend entropy search, a variant of Bayesian
optimization, such that it can be used for active contextual policy search
where the agent selects those tasks during training in which it expects to
learn the most. Empirical results in simulation suggest that this allows
learning successful behavior with less trials.
Higher-order Segmentation via Multicuts
Authors: Joerg Hendrik Kappes, Markus Speth, Gerhard Reinelt, Christoph Schnoerr
Date: 2013-05-28 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Multicuts enable to conveniently represent discrete graphical models for
unsupervised and supervised image segmentation, in the case of local energy
functions that exhibit symmetries. The basic Potts model and natural extensions
thereof to higher-order models provide a prominent class of such objectives,
that cover a broad range of segmentation problems relevant to image analysis
and computer vision. We exhibit a way to systematically take into account such
higher-order terms for computational inference. Furthermore, we present results
of a comprehensive and competitive numerical evaluation of a variety of
dedicated cutting-plane algorithms. Our approach enables the globally optimal
evaluation of a significant subset of these models, without compromising
runtime. Polynomially solvable relaxations are studied as well, along with
advanced rounding schemes for post-processing.
Analyzing Stability of Convolutional Neural Networks in the Frequency Domain
Authors: Elnaz J. Heravi, Hamed H. Aghdam, Domenec Puig
Date: 2015-11-10 00:00:00

Understanding the internal process of ConvNets is commonly done using
visualization techniques. However, these techniques do not usually provide a
tool for estimating the stability of a ConvNet against noise. In this paper, we
show how to analyze a ConvNet in the frequency domain using a 4-dimensional
visualization technique. Using the frequency domain analysis, we show the
reason that a ConvNet might be sensitive to a very low magnitude additive
noise. Our experiments on a few ConvNets trained on different datasets revealed
that convolution kernels of a trained ConvNet usually pass most of the
frequencies and they are not able to effectively eliminate the effect of high
frequencies. Our next experiments shows that a convolution kernel which has a
more concentrated frequency response could be more stable. Finally, we show
that fine-tuning a ConvNet using a training set augmented with noisy images can
produce more stable ConvNets.
Probabilistic Segmentation via Total Variation Regularization
Authors: Matt Wytock, J. Zico Kolter
Date: 2015-11-16 00:00:00
Tasks: Density Estimation, Segmentation, Time Series, Time Series Analysis

We present a convex approach to probabilistic segmentation and modeling of
time series data. Our approach builds upon recent advances in multivariate
total variation regularization, and seeks to learn a separate set of parameters
for the distribution over the observations at each time point, but with an
additional penalty that encourages the parameters to remain constant over time.
We propose efficient optimization methods for solving the resulting (large)
optimization problems, and a two-stage procedure for estimating recurring
clusters under such models, based upon kernel density estimation. Finally, we
show on a number of real-world segmentation tasks, the resulting methods often
perform as well or better than existing latent variable models, while being
substantially easier to train.
Detecting Interrogative Utterances with Recurrent Neural Networks
Authors: Junyoung Chung, Jacob Devlin, Hany Hassan Awadalla
Date: 2015-11-03 00:00:00
Tasks: General Classification

In this paper, we explore different neural network architectures that can
predict if a speaker of a given utterance is asking a question or making a
statement. We com- pare the outcomes of regularization methods that are
popularly used to train deep neural networks and study how different context
functions can affect the classification performance. We also compare the
efficacy of gated activation functions that are favorably used in recurrent
neural networks and study how to combine multimodal inputs. We evaluate our
models on two multimodal datasets: MSR-Skype and CALLHOME.
Learning Mid-level Words on Riemannian Manifold for Action Recognition
Authors: Mengyi Liu, Ruiping Wang, Shiguang Shan, Xilin Chen
Date: 2015-11-16 00:00:00
Tasks: Action Recognition, Clustering, Temporal Action Localization

Human action recognition remains a challenging task due to the various
sources of video data and large intra-class variations. It thus becomes one of
the key issues in recent research to explore effective and robust
representation to handle such challenges. In this paper, we propose a novel
representation approach by constructing mid-level words in videos and encoding
them on Riemannian manifold. Specifically, we first conduct a global alignment
on the densely extracted low-level features to build a bank of corresponding
feature groups, each of which can be statistically modeled as a mid-level word
lying on some specific Riemannian manifold. Based on these mid-level words, we
construct intrinsic Riemannian codebooks by employing K-Karcher-means
clustering and Riemannian Gaussian Mixture Model, and consequently extend the
Riemannian manifold version of three well studied encoding methods in Euclidean
space, i.e. Bag of Visual Words (BoVW), Vector of Locally Aggregated
Descriptors (VLAD), and Fisher Vector (FV), to obtain the final action video
representations. Our method is evaluated in two tasks on four popular realistic
datasets: action recognition on YouTube, UCF50, HMDB51 databases, and action
similarity labeling on ASLAN database. In all cases, the reported results
achieve very competitive performance with those most recent state-of-the-art
works.
Causal interpretation rules for encoding and decoding models in neuroimaging
Authors: Sebastian Weichwald, Timm Meyer, Ozan Özdenizci, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup
Date: 2015-11-15 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Causal terminology is often introduced in the interpretation of encoding and
decoding models trained on neuroimaging data. In this article, we investigate
which causal statements are warranted and which ones are not supported by
empirical evidence. We argue that the distinction between encoding and decoding
models is not sufficient for this purpose: relevant features in encoding and
decoding models carry a different meaning in stimulus- and in response-based
experimental paradigms. We show that only encoding models in the stimulus-based
setting support unambiguous causal interpretations. By combining encoding and
decoding models trained on the same data, however, we obtain insights into
causal relations beyond those that are implied by each individual model type.
We illustrate the empirical relevance of our theoretical findings on EEG data
recorded during a visuo-motor learning task.
Expressive recommender systems through normalized nonnegative models
Authors: Cyril Stark
Date: 2015-11-15 00:00:00
Tasks: Recommendation Systems

We introduce normalized nonnegative models (NNM) for explorative data
analysis. NNMs are partial convexifications of models from probability theory.
We demonstrate their value at the example of item recommendation. We show that
NNM-based recommender systems satisfy three criteria that all recommender
systems should ideally satisfy: high predictive power, computational
tractability, and expressive representations of users and items. Expressive
user and item representations are important in practice to succinctly summarize
the pool of customers and the pool of items. In NNMs, user representations are
expressive because each user's preference can be regarded as normalized mixture
of preferences of stereotypical users. The interpretability of item and user
representations allow us to arrange properties of items (e.g., genres of movies
or topics of documents) or users (e.g., personality traits) hierarchically.
An Iterative Reweighted Method for Tucker Decomposition of Incomplete Multiway Tensors
Authors: Linxiao Yang, Jun Fang, Hongbin Li, Bing Zeng
Date: 2015-11-15 00:00:00
Tasks: Image Inpainting, Recommendation Systems

We consider the problem of low-rank decomposition of incomplete multiway
tensors. Since many real-world data lie on an intrinsically low dimensional
subspace, tensor low-rank decomposition with missing entries has applications
in many data analysis problems such as recommender systems and image
inpainting. In this paper, we focus on Tucker decomposition which represents an
Nth-order tensor in terms of N factor matrices and a core tensor via
multilinear operations. To exploit the underlying multilinear low-rank
structure in high-dimensional datasets, we propose a group-based log-sum
penalty functional to place structural sparsity over the core tensor, which
leads to a compact representation with smallest core tensor. The method for
Tucker decomposition is developed by iteratively minimizing a surrogate
function that majorizes the original objective function, which results in an
iterative reweighted process. In addition, to reduce the computational
complexity, an over-relaxed monotone fast iterative shrinkage-thresholding
technique is adapted and embedded in the iterative reweighted process. The
proposed method is able to determine the model complexity (i.e. multilinear
rank) in an automatic way. Simulation results show that the proposed algorithm
offers competitive performance compared with other existing algorithms.
Separation Surfaces in the Spectral TV Domain for Texture Decomposition
Authors: Dikla Horesh, Guy Gilboa
Date: 2015-11-15 00:00:00

In this paper we introduce a novel notion of separation surfaces for image
decomposition. A surface is embedded in the spectral total-variation (TV) three
dimensional domain and encodes a spatially-varying separation scale. The method
allows good separation of textures with gradually varying pattern-size,
pattern-contrast or illumination. The recently proposed total variation
spectral framework is used to decompose the image into a continuum of textural
scales. A desired texture, within a scale range, is found by fitting a surface
to the local maximal responses in the spectral domain. A band above and below
the surface, referred to as the \textit{Texture Stratum}, defines for each
pixel the adaptive scale-range of the texture. Based on the decomposition an
application is proposed which can attenuate or enhance textures in the image in
a very natural and visually convincing manner.
Semi-Inner-Products for Convex Functionals and Their Use in Image Decomposition
Authors: Guy Gilboa
Date: 2015-11-15 00:00:00

Semi-inner-products in the sense of Lumer are extended to convex functionals.
This yields a Hilbert-space like structure to convex functionals in Banach
spaces. In particular, a general expression for semi-inner-products with
respect to one homogeneous functionals is given. Thus one can use the new
operator for the analysis of total variation and higher order functionals like
total-generalized-variation (TGV). Having a semi-inner-product, an angle
between functions can be defined in a straightforward manner. It is shown that
in the one homogeneous case the Bregman distance can be expressed in terms of
this newly defined angle. In addition, properties of the semi-inner-product of
nonlinear eigenfunctions induced by the functional are derived. We use this
construction to state a sufficient condition for a perfect decomposition of two
signals and suggest numerical measures which indicate when those conditions are
approximately met.
Breaking Sticks and Ambiguities with Adaptive Skip-gram
Authors: Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry Vetrov
Date: 2015-02-25 00:00:00
Tasks: Word Sense Induction

Recently proposed Skip-gram model is a powerful method for learning
high-dimensional word representations that capture rich semantic relationships
between words. However, Skip-gram as well as most prior work on learning word
representations does not take into account word ambiguity and maintain only
single representation per word. Although a number of Skip-gram modifications
were proposed to overcome this limitation and learn multi-prototype word
representations, they either require a known number of word meanings or learn
them using greedy heuristic approaches. In this paper we propose the Adaptive
Skip-gram model which is a nonparametric Bayesian extension of Skip-gram
capable to automatically learn the required number of representations for all
words at desired semantic resolution. We derive efficient online variational
learning algorithm for the model and empirically demonstrate its efficiency on
word-sense induction task.
Uncovering Temporal Context for Video Question and Answering
Authors: Linchao Zhu, Zhongwen Xu, Yi Yang, Alexander G. Hauptmann
Date: 2015-11-15 00:00:00
Tasks: Decoder, Multiple-choice, Question Answering, Video Question Answering

In this work, we introduce Video Question Answering in temporal domain to
infer the past, describe the present and predict the future. We present an
encoder-decoder approach using Recurrent Neural Networks to learn temporal
structures of videos and introduce a dual-channel ranking loss to answer
multiple-choice questions. We explore approaches for finer understanding of
video content using question form of "fill-in-the-blank", and managed to
collect 109,895 video clips with duration over 1,000 hours from TACoS, MPII-MD,
MEDTest 14 datasets, while the corresponding 390,744 questions are generated
from annotations. Extensive experiments demonstrate that our approach
significantly outperforms the compared baselines.
A System for Extracting Sentiment from Large-Scale Arabic Social Data
Authors: Hao Wang, Vijay R. Bommireddipalli, Ayman Hanafy, Mohamed Bahgat, Sara Noeman, Ossama S. Emam
Date: 2015-11-15 00:00:00
Tasks: Arabic Sentiment Analysis, General Classification, Sentiment Analysis, Sentiment Classification

Social media data in Arabic language is becoming more and more abundant. It
is a consensus that valuable information lies in social media data. Mining this
data and making the process easier are gaining momentum in the industries. This
paper describes an enterprise system we developed for extracting sentiment from
large volumes of social data in Arabic dialects. First, we give an overview of
the Big Data system for information extraction from multilingual social data
from a variety of sources. Then, we focus on the Arabic sentiment analysis
capability that was built on top of the system including normalizing written
Arabic dialects, building sentiment lexicons, sentiment classification, and
performance evaluation. Lastly, we demonstrate the value of enriching sentiment
results with user profiles in understanding sentiments of a specific user
group.
Implementation and comparative quantitative assessment of different multispectral image pansharpening approches
Authors: Shailesh Panchal, Rajesh Thakker
Date: 2015-11-15 00:00:00
Tasks: Earth Observation, Pansharpening

In remote sensing, images acquired by various earth observation satellites
tend to have either a high spatial and low spectral resolution or vice versa.
Pansharpening is a technique which aims to improve spatial resolution of
multispectral image. The challenges involve in the pansharpening are not only
to improve the spatial resolution but also to preserve spectral quality of the
multispectral image. In this paper, various pansharpening algorithms are
discussed and classified based on approaches they have adopted. Using MATLAB
image processing toolbox, several state-of-art pan-sharpening algorithms are
implemented. Quality of pansharpened images are assessed visually and
quantitatively. Correlation coefficient (CC), Root mean square error (RMSE),
Relative average spectral error (RASE) and Universal quality index (Q) indices
are used to easure spectral quality while to spatial-CC (SCC) quantitative
parameter is used for spatial quality measurement. Finally, the paper is
concluded with useful remarks.
Character-based Neural Machine Translation
Authors: Wang Ling, Isabel Trancoso, Chris Dyer, Alan W. black
Date: 2015-11-14 00:00:00
Tasks: Machine Translation, Translation

We introduce a neural machine translation model that views the input and
output sentences as sequences of characters rather than words. Since word-level
information provides a crucial source of bias, our input model composes
representations of character sequences into representations of words (as
determined by whitespace boundaries), and then these are translated using a
joint attention/translation model. In the target language, the translation is
modeled as a sequence of word vectors, but each word is generated one character
at a time, conditional on the previous character generations in each word. As
the representation and generation of words is performed at the character level,
our model is capable of interpreting and generating unseen word forms. A
secondary benefit of this approach is that it alleviates much of the challenges
associated with preprocessing/tokenization of the source and target languages.
We show that our model can achieve translation results that are on par with
conventional word-based models.
Fast Proximal Linearized Alternating Direction Method of Multiplier with Parallel Splitting
Authors: Canyi Lu, Huan Li, Zhouchen Lin, Shuicheng Yan
Date: 2015-11-14 00:00:00

The Augmented Lagragian Method (ALM) and Alternating Direction Method of
Multiplier (ADMM) have been powerful optimization methods for general convex
programming subject to linear constraint. We consider the convex problem whose
objective consists of a smooth part and a nonsmooth but simple part. We propose
the Fast Proximal Augmented Lagragian Method (Fast PALM) which achieves the
convergence rate $O(1/K^2)$, compared with $O(1/K)$ by the traditional PALM. In
order to further reduce the per-iteration complexity and handle the
multi-blocks problem, we propose the Fast Proximal ADMM with Parallel Splitting
(Fast PL-ADMM-PS) method. It also partially improves the rate related to the
smooth part of the objective function. Experimental results on both synthesized
and real world data demonstrate that our fast methods significantly improve the
previous PALM and ADMM.