pwc-archive/papers-with-abstracts huggingface dataset:
Partitioning Well-Clustered Graphs: Spectral Clustering Works!
Authors: Richard Peng, He Sun, Luca Zanetti
Date: 2014-11-07 00:00:00
Tasks: Clustering

In this paper we study variants of the widely used spectral clustering that
partitions a graph into k clusters by (1) embedding the vertices of a graph
into a low-dimensional space using the bottom eigenvectors of the Laplacian
matrix, and (2) grouping the embedded points into k clusters via k-means
algorithms. We show that, for a wide class of graphs, spectral clustering gives
a good approximation of the optimal clustering. While this approach was
proposed in the early 1990s and has comprehensive applications, prior to our
work similar results were known only for graphs generated from stochastic
models.
  We also give a nearly-linear time algorithm for partitioning well-clustered
graphs based on computing a matrix exponential and approximate nearest neighbor
data structures.
On Valid Optimal Assignment Kernels and Applications to Graph Classification
Authors: Nils M. Kriege, Pierre-Louis Giscard, Richard C. Wilson
Date: 2016-06-03 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Classification, General Classification, Graph Classification, valid

The success of kernel methods has initiated the design of novel positive
semidefinite functions, in particular for structured data. A leading design
paradigm for this is the convolution kernel, which decomposes structured
objects into their parts and sums over all pairs of parts. Assignment kernels,
in contrast, are obtained from an optimal bijection between parts, which can
provide a more valid notion of similarity. In general however, optimal
assignments yield indefinite functions, which complicates their use in kernel
methods. We characterize a class of base kernels used to compare parts that
guarantees positive semidefinite optimal assignment kernels. These base kernels
give rise to hierarchies from which the optimal assignment kernels are computed
in linear time by histogram intersection. We apply these results by developing
the Weisfeiler-Lehman optimal assignment kernel for graphs. It provides high
classification accuracy on widely-used benchmark data sets improving over the
original Weisfeiler-Lehman kernel.
A New Method for Removing the Moire' Pattern from Images
Authors: Seyede Mahya Hazavei, Hamid Reza Shahdoosti
Date: 2017-01-31 00:00:00
Tasks: Denoising

During the last decades, denoising methods have attracted much attention of
researchers. The conventional method for removing the Moire' pattern from
images is using notch filters in the Frequency-domain. In this paper a new
method is proposed that can achieve a better performance in comparison with the
traditional method. Median filter is used in some part of spectrum of noisy
images to reduce the noise. At the second part of this paper, to demonstrate
the robustness of the proposed method, it is implemented for some noisy images
that have moire' pattern. Experiments on noisy images with different
characteristics show that the proposed method increases the PSNR values
compared with previous methods.
Finding Significant Fourier Coefficients: Clarifications, Simplifications, Applications and Limitations
Authors: Steven D. Galbraith, Joel Laity, Barak Shani
Date: 2016-07-06 00:00:00
Tasks: Open-Ended Question Answering

Ideas from Fourier analysis have been used in cryptography for the last three
decades. Akavia, Goldwasser and Safra unified some of these ideas to give a
complete algorithm that finds significant Fourier coefficients of functions on
any finite abelian group. Their algorithm stimulated a lot of interest in the
cryptography community, especially in the context of `bit security'. This
manuscript attempts to be a friendly and comprehensive guide to the tools and
results in this field. The intended readership is cryptographers who have heard
about these tools and seek an understanding of their mechanics and their
usefulness and limitations. A compact overview of the algorithm is presented
with emphasis on the ideas behind it. We show how these ideas can be extended
to a `modulus-switching' variant of the algorithm. We survey some applications
of this algorithm, and explain that several results should be taken in the
right context. In particular, we point out that some of the most important bit
security problems are still open. Our original contributions include: a
discussion of the limitations on the usefulness of these tools; an answer to an
open question about the modular inversion hidden number problem.
Computational Techniques in Multispectral Image Processing: Application to the Syriac Galen Palimpsest
Authors: Corneliu Arsene, Peter Pormann, William Sellers, Siam Bhayro
Date: 2017-01-31 00:00:00
Tasks: Dimensionality Reduction, Hyperspectral image analysis

Multispectral and hyperspectral image analysis has experienced much
development in the last decade. The application of these methods to palimpsests
has produced significant results, enabling researchers to recover texts that
would be otherwise lost under the visible overtext, by improving the contrast
between the undertext and the overtext. In this paper we explore an extended
number of multispectral and hyperspectral image analysis methods, consisting of
supervised and unsupervised dimensionality reduction techniques, on a part of
the Syriac Galen Palimpsest dataset (www.digitalgalen.net). Of this extended
set of methods, eight methods gave good results: three were supervised methods
Generalized Discriminant Analysis (GDA), Linear Discriminant Analysis (LDA),
and Neighborhood Component Analysis (NCA); and the other five methods were
unsupervised methods (but still used in a supervised way) Gaussian Process
Latent Variable Model (GPLVM), Isomap, Landmark Isomap, Principal Component
Analysis (PCA), and Probabilistic Principal Component Analysis (PPCA). The
relative success of these methods was determined visually, using color
pictures, on the basis of whether the undertext was distinguishable from the
overtext, resulting in the following ranking of the methods: LDA, NCA, GDA,
Isomap, Landmark Isomap, PPCA, PCA, and GPLVM. These results were compared with
those obtained using the Canonical Variates Analysis (CVA) method on the same
dataset, which showed remarkably accuracy (LDA is a particular case of CVA
where the objects are classified to two classes).
Parsimonious Inference on Convolutional Neural Networks: Learning and applying on-line kernel activation rules
Authors: I. Theodorakopoulos, V. Pothos, D. Kastaniotis, N. Fragoulis
Date: 2017-01-18 00:00:00

A new, radical CNN design approach is presented in this paper, considering
the reduction of the total computational load during inference. This is
achieved by a new holistic intervention on both the CNN architecture and the
training procedure, which targets to the parsimonious inference by learning to
exploit or remove the redundant capacity of a CNN architecture. This is
accomplished, by the introduction of a new structural element that can be
inserted as an add-on to any contemporary CNN architecture, whilst preserving
or even improving its recognition accuracy. Our approach formulates a
systematic and data-driven method for developing CNNs that are trained to
eventually change size and form in real-time during inference, targeting to the
smaller possible computational footprint. Results are provided for the optimal
implementation on a few modern, high-end mobile computing platforms indicating
a significant speed-up of up to x3 times.
On the Semantics and Complexity of Probabilistic Logic Programs
Authors: Fabio Gagliardi Cozman, Denis Deratani Mauá
Date: 2017-01-31 00:00:00

We examine the meaning and the complexity of probabilistic logic programs
that consist of a set of rules and a set of independent probabilistic facts
(that is, programs based on Sato's distribution semantics). We focus on two
semantics, respectively based on stable and on well-founded models. We show
that the semantics based on stable models (referred to as the "credal
semantics") produces sets of probability models that dominate infinitely
monotone Choquet capacities, we describe several useful consequences of this
result. We then examine the complexity of inference with probabilistic logic
programs. We distinguish between the complexity of inference when a
probabilistic program and a query are given (the inferential complexity), and
the complexity of inference when the probabilistic program is fixed and the
query is given (the query complexity, akin to data complexity as used in
database theory). We obtain results on the inferential and query complexity for
acyclic, stratified, and cyclic propositional and relational programs,
complexity reaches various levels of the counting hierarchy and even
exponential levels.
'Viral' Turing Machines, Computation from Noise and Combinatorial Hierarchies
Authors: T. E. Raptis
Date: 2017-01-31 00:00:00
Tasks: Form

The interactive computation paradigm is reviewed and a particular example is extended to form the stochastic analog of a computational process via a transcription of a minimal Turing Machine into an equivalent asynchronous Cellular Automaton with an exponential waiting times distribution of effective transitions. Furthermore, a special toolbox for analytic derivation of recursive relations of important statistical and other quantities is introduced in the form of an Inductive Combinatorial Hierarchy.
Deep Multitask Architecture for Integrated 2D and 3D Human Sensing
Authors: Alin-Ionut Popa, Mihai Zanfir, Cristian Sminchisescu
Date: 2017-01-31 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Human Pose Estimation

We propose a deep multitask architecture for \emph{fully automatic 2d and 3d
human sensing} (DMHS), including \emph{recognition and reconstruction}, in
\emph{monocular images}. The system computes the figure-ground segmentation,
semantically identifies the human body parts at pixel level, and estimates the
2d and 3d pose of the person. The model supports the joint training of all
components by means of multi-task losses where early processing stages
recursively feed into advanced ones for increasingly complex calculations,
accuracy and robustness. The design allows us to tie a complete training
protocol, by taking advantage of multiple datasets that would otherwise
restrictively cover only some of the model components: complex 2d image data
with no body part labeling and without associated 3d ground truth, or complex
3d data with limited 2d background variability. In detailed experiments based
on several challenging 2d and 3d datasets (LSP, HumanEva, Human3.6M), we
evaluate the sub-structures of the model, the effect of various types of
training data in the multitask loss, and demonstrate that state-of-the-art
results can be achieved at all processing levels. We also show that in the wild
our monocular RGB architecture is perceptually competitive to a state-of-the
art (commercial) Kinect system based on RGB-D data.
Machine learning based compact photonic structure design for strong light confinement
Authors: Mirbek Turduev, Çağrı Latifoğlu, İbrahim Halil Giden, Y. Sinan Hanay
Date: 2017-01-31 00:00:00
Tasks: BIG-bench Machine Learning

We present a novel approach based on machine learning for designing photonic
structures. In particular, we focus on strong light confinement that allows the
design of an efficient free-space-to-waveguide coupler which is made of Si-
slab overlying on the top of silica substrate. The learning algorithm is
implemented using bitwise square Si- cells and the whole optimized device has a
footprint of $\boldsymbol{2 \, \mu m \times 1\, \mu m}$, which is the smallest
size ever achieved numerically. To find the effect of Si- slab thickness on the
sub-wavelength focusing and strong coupling characteristics of optimized
photonic structure, we carried out three-dimensional time-domain numerical
calculations. Corresponding optimum values of full width at half maximum and
coupling efficiency were calculated as $\boldsymbol{0.158 \lambda}$ and
$\boldsymbol{-1.87\,dB}$ with slab thickness of $\boldsymbol{280nm}$. Compared
to the conventional counterparts, the optimized lens and coupler designs are
easy-to-fabricate via optical lithography techniques, quite compact, and can
operate at telecommunication wavelengths. The outcomes of the presented study
show that machine learning can be beneficial for efficient photonic designs in
various potential applications such as polarization-division, beam manipulation
and optical interconnects.
Towards Adversarial Retinal Image Synthesis
Authors: Pedro Costa, Adrian Galdran, Maria Inês Meyer, Michael David Abràmoff, Meindert Niemeijer, Ana Maria Mendonça, Aurélio Campilho
Date: 2017-01-31 00:00:00
Tasks: Anatomy, Image Generation, Image-to-Image Translation, Medical Image Generation, Translation

Synthesizing images of the eye fundus is a challenging task that has been
previously approached by formulating complex models of the anatomy of the eye.
New images can then be generated by sampling a suitable parameter space. In
this work, we propose a method that learns to synthesize eye fundus images
directly from data. For that, we pair true eye fundus images with their
respective vessel trees, by means of a vessel segmentation technique. These
pairs are then used to learn a mapping from a binary vessel tree to a new
retinal image. For this purpose, we use a recent image-to-image translation
technique, based on the idea of adversarial learning. Experimental results show
that the original and the generated images are visually different in terms of
their global appearance, in spite of sharing the same vessel tree.
Additionally, a quantitative quality analysis of the synthetic retinal images
confirms that the produced images retain a high proportion of the true image
set quality.
Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection
Authors: Nhan Truong, Levin Kuhlmann, Mohammad Reza Bonyadi, Jiawei Yang, Andrew Faulks, Omid Kavehei
Date: 2017-01-31 00:00:00
Tasks: channel selection, Computational Efficiency, General Classification, Seizure Detection, Specificity

Detecting seizure using brain neuroactivations recorded by intracranial
electroencephalogram (iEEG) has been widely used for monitoring, diagnosing,
and closed-loop therapy of epileptic patients, however, computational
efficiency gains are needed if state-of-the-art methods are to be implemented
in implanted devices. We present a novel method for automatic seizure detection
based on iEEG data that outperforms current state-of-the-art seizure detection
methods in terms of computational efficiency while maintaining the accuracy.
The proposed algorithm incorporates an automatic channel selection (ACS) engine
as a pre-processing stage to the seizure detection procedure. The ACS engine
consists of supervised classifiers which aim to find iEEGchannelswhich
contribute the most to a seizure. Seizure detection stage involves feature
extraction and classification. Feature extraction is performed in both
frequency and time domains where spectral power and correlation between channel
pairs are calculated. Random Forest is used in classification of interictal,
ictal and early ictal periods of iEEG signals. Seizure detection in this paper
is retrospective and patient-specific. iEEG data is accessed via Kaggle,
provided by International Epilepsy Electro-physiology Portal. The dataset
includes a training set of 6.5 hours of interictal data and 41 minin ictal data
and a test set of 9.14 hours. Compared to the state-of-the-art on the same
dataset, we achieve 49.4% increase in computational efficiency and 400 mins
better in average for detection delay. The proposed model is able to detect a
seizure onset at 91.95% sensitivity and 94.05% specificity with a mean
detection delay of 2.77 s. The area under the curve (AUC) is 96.44%, that is
comparable to the current state-of-the-art with AUC of 96.29%.
Variable selection for clustering with Gaussian mixture models: state of the art
Authors: Abdelghafour Talibi, Boujemâa Achchab, Rafik Lasri
Date: 2017-01-31 00:00:00
Tasks: Clustering, Variable Selection

The mixture models have become widely used in clustering, given its
probabilistic framework in which its based, however, for modern databases that
are characterized by their large size, these models behave disappointingly in
setting out the model, making essential the selection of relevant variables for
this type of clustering. After recalling the basics of clustering based on a
model, this article will examine the variable selection methods for model-based
clustering, as well as presenting opportunities for improvement of these
methods.
Deep Submodular Functions
Authors: Jeffrey Bilmes, Wenruo Bai
Date: 2017-01-31 00:00:00
Tasks: Descriptive

We start with an overview of a class of submodular functions called SCMMs
(sums of concave composed with non-negative modular functions plus a final
arbitrary modular). We then define a new class of submodular functions we call
{\em deep submodular functions} or DSFs. We show that DSFs are a flexible
parametric family of submodular functions that share many of the properties and
advantages of deep neural networks (DNNs). DSFs can be motivated by considering
a hierarchy of descriptive concepts over ground elements and where one wishes
to allow submodular interaction throughout this hierarchy. Results in this
paper show that DSFs constitute a strictly larger class of submodular functions
than SCMMs. We show that, for any integer $k>0$, there are $k$-layer DSFs that
cannot be represented by a $k'$-layer DSF for any $k'<k$. This implies that,
like DNNs, there is a utility to depth, but unlike DNNs, the family of DSFs
strictly increase with depth. Despite this, we show (using a "backpropagation"
like method) that DSFs, even with arbitrarily large $k$, do not comprise all
submodular functions. In offering the above results, we also define the notion
of an antitone superdifferential of a concave function and show how this
relates to submodular functions (in general), DSFs (in particular), negative
second-order partial derivatives, continuous submodularity, and concave
extensions. To further motivate our analysis, we provide various special case
results from matroid theory, comparing DSFs with forms of matroid rank, in
particular the laminar matroid. Lastly, we discuss strategies to learn DSFs,
and define the classes of deep supermodular functions, deep difference of
submodular functions, and deep multivariate submodular functions, and discuss
where these can be useful in applications.
Co-segmentation for Space-Time Co-located Collections
Authors: Hadar Averbuch-Elor, Johannes Kopf, Tamir Hazan, Daniel Cohen-Or
Date: 2017-01-31 00:00:00
Tasks: Object, Segmentation

We present a co-segmentation technique for space-time co-located image
collections. These prevalent collections capture various dynamic events,
usually by multiple photographers, and may contain multiple co-occurring
objects which are not necessarily part of the intended foreground object,
resulting in ambiguities for traditional co-segmentation techniques. Thus, to
disambiguate what the common foreground object is, we introduce a
weakly-supervised technique, where we assume only a small seed, given in the
form of a single segmented image. We take a distributed approach, where local
belief models are propagated and reinforced with similar images. Our technique
progressively expands the foreground and background belief models across the
entire collection. The technique exploits the power of the entire set of image
without building a global model, and thus successfully overcomes large
variability in appearance of the common foreground object. We demonstrate that
our method outperforms previous co-segmentation techniques on challenging
space-time co-located collections, including dense benchmark datasets which
were adapted for our novel problem setting.
Efficient Learning in Large-Scale Combinatorial Semi-Bandits
Authors: Zheng Wen, Branislav Kveton, Azin Ashkan
Date: 2014-06-28 00:00:00
Tasks: Thompson Sampling

A stochastic combinatorial semi-bandit is an online learning problem where at
each step a learning agent chooses a subset of ground items subject to
combinatorial constraints, and then observes stochastic weights of these items
and receives their sum as a payoff. In this paper, we consider efficient
learning in large-scale combinatorial semi-bandits with linear generalization,
and as a solution, propose two learning algorithms called Combinatorial Linear
Thompson Sampling (CombLinTS) and Combinatorial Linear UCB (CombLinUCB). Both
algorithms are computationally efficient as long as the offline version of the
combinatorial problem can be solved efficiently. We establish that CombLinTS
and CombLinUCB are also provably statistically efficient under reasonable
assumptions, by developing regret bounds that are independent of the problem
scale (number of items) and sublinear in time. We also evaluate CombLinTS on a
variety of problems with thousands of items. Our experiment results demonstrate
that CombLinTS is scalable, robust to the choice of algorithm parameters, and
significantly outperforms the best of our baselines.
Feature Selection based on PCA and PSO for Multimodal Medical Image Fusion using DTCWT
Authors: Padmavathi K, Mahima Bhat, Maya V Karki
Date: 2017-01-31 00:00:00
Tasks: feature selection, Medical Diagnosis, SSIM

Multimodal medical image fusion helps to increase efficiency in medical
diagnosis. This paper presents multimodal medical image fusion by selecting
relevant features using Principle Component Analysis (PCA) and Particle Swarm
Optimization techniques (PSO). DTCWT is used for decomposition of the images
into low and high frequency coefficients. Fusion rules such as combination of
minimum, maximum and simple averaging are applied to approximate and detailed
coefficients. The fused image is reconstructed by inverse DTCWT. Performance
metrics are evaluated and it shows that DTCWT-PCA performs better than
DTCWT-PSO in terms of Structural Similarity Index Measure (SSIM) and Cross
Correlation (CC). Computation time and feature vector size is reduced in
DTCWT-PCA compared to DTCWT-PSO for feature selection which proves robustness
and storage capacity.
SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation
Authors: Moustafa Alzantot, Supriyo Chakraborty, Mani B. Srivastava
Date: 2017-01-31 00:00:00
Tasks: Deep Learning

Our ability to synthesize sensory data that preserves specific statistical
properties of the real data has had tremendous implications on data privacy and
big data analytics. The synthetic data can be used as a substitute for
selective real data segments,that are sensitive to the user, thus protecting
privacy and resulting in improved analytics.However, increasingly adversarial
roles taken by data recipients such as mobile apps, or other cloud-based
analytics services, mandate that the synthetic data, in addition to preserving
statistical properties, should also be difficult to distinguish from the real
data. Typically, visual inspection has been used as a test to distinguish
between datasets. But more recently, sophisticated classifier models
(discriminators), corresponding to a set of events, have also been employed to
distinguish between synthesized and real data. The model operates on both
datasets and the respective event outputs are compared for consistency. In this
paper, we take a step towards generating sensory data that can pass a deep
learning based discriminator model test, and make two specific contributions:
first, we present a deep learning based architecture for synthesizing sensory
data. This architecture comprises of a generator model, which is a stack of
multiple Long-Short-Term-Memory (LSTM) networks and a Mixture Density Network.
second, we use another LSTM network based discriminator model for
distinguishing between the true and the synthesized data. Using a dataset of
accelerometer traces, collected using smartphones of users doing their daily
activities, we show that the deep learning based discriminator model can only
distinguish between the real and synthesized traces with an accuracy in the
neighborhood of 50%.