pwc-archive/papers-with-abstracts huggingface dataset:
Maximum entropy based non-negative optoacoustic tomographic image reconstruction
Authors: Jaya Prakash, Subhamoy Mandal, Daniel Razansky, Vasilis Ntziachristos
Date: 2017-07-26 00:00:00
Tasks: Image Reconstruction

Objective:Optoacoustic (photoacoustic) tomography is aimed at reconstructing
maps of the initial pressure rise induced by the absorption of light pulses in
tissue. In practice, due to inaccurate assumptions in the forward model, noise
and other experimental factors, the images are often afflicted by artifacts,
occasionally manifested as negative values. The aim of the work is to develop
an inversion method which reduces the occurrence of negative values and
improves the quantitative performance of optoacoustic imaging. Methods: We
present a novel method for optoacoustic tomography based on an entropy
maximization algorithm, which uses logarithmic regularization for attaining
non-negative reconstructions. The reconstruction image quality is further
improved using structural prior based fluence correction. Results: We report
the performance achieved by the entropy maximization scheme on numerical
simulation, experimental phantoms and in-vivo samples. Conclusion: The proposed
algorithm demonstrates superior reconstruction performance by delivering
non-negative pixel values with no visible distortion of anatomical structures.
Significance: Our method can enable quantitative optoacoustic imaging, and has
the potential to improve pre-clinical and translational imaging applications.
Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network
Authors: Akm Ashiquzzaman, Abdul Kawsar Tushar, Md. Rashedul Islam, Jong-Myon Kim
Date: 2017-07-26 00:00:00
Tasks: Diabetes Prediction, Prediction, Prognosis

Augmented accuracy in prediction of diabetes will open up new frontiers in
health prognostics. Data overfitting is a performance-degrading issue in
diabetes prognosis. In this study, a prediction system for the disease of
diabetes is pre-sented where the issue of overfitting is minimized by using the
dropout method. Deep learning neural network is used where both fully connected
layers are fol-lowed by dropout layers. The output performance of the proposed
neural network is shown to have outperformed other state-of-art methods and it
is recorded as by far the best performance for the Pima Indians Diabetes Data
Set.
A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks
Authors: Abdul Kawsar Tushar, Akm Ashiquzzaman, Afia Afrin, Md. Rashedul Islam
Date: 2017-07-26 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Transfer Learning

Increased accuracy in predictive models for handwritten character recognition
will open up new frontiers for optical character recognition. Major drawbacks
of predictive machine learning models are headed by the elongated training time
taken by some models, and the requirement that training and test data be in the
same feature space and consist of the same distribution. In this study, these
obstacles are minimized by presenting a model for transferring knowledge from
one task to another. This model is presented for the recognition of handwritten
numerals in Indic languages. The model utilizes convolutional neural networks
with backpropagation for error reduction and dropout for data overfitting. The
output performance of the proposed neural network is shown to have closely
matched other state-of-the-art methods using only a fraction of time used by
the state-of-the-arts.
Sequential design of experiments to estimate a probability of exceeding a threshold in a multi-fidelity stochastic simulator
Authors: Rémi Stroh, Séverine Demeyer, Nicolas Fischer, Julien Bect, Emmanuel Vazquez
Date: 2017-07-26 00:00:00

In this article, we consider a stochastic numerical simulator to assess the
impact of some factors on a phenomenon. The simulator is seen as a black box
with inputs and outputs. The quality of a simulation, hereafter referred to as
fidelity, is assumed to be tunable by means of an additional input of the
simulator (e.g., a mesh size parameter): high-fidelity simulations provide more
accurate results, but are time-consuming. Using a limited computation-time
budget, we want to estimate, for any value of the physical inputs, the
probability that a certain scalar output of the simulator will exceed a given
critical threshold at the highest fidelity level. The problem is addressed in a
Bayesian framework, using a Gaussian process model of the multi-fidelity
simulator. We consider a Bayesian estimator of the probability, together with
an associated measure of uncertainty, and propose a new multi-fidelity
sequential design strategy, called Maximum Speed of Uncertainty Reduction
(MSUR), to select the value of physical inputs and the fidelity level of new
simulations. The MSUR strategy is tested on an example.
Prediction of amino acid side chain conformation using a deep neural network
Authors: Ke Liu, Xiangyan Sun, Jun Ma, Zhenyu Zhou, Qilin Dong, Shengwen Peng, Junqiu Wu, Suocheng Tan, Günter Blobel, Jie Fan
Date: 2017-07-26 00:00:00
Tasks: Prediction, Protein Design, Protein Folding

A deep neural network based architecture was constructed to predict amino
acid side chain conformation with unprecedented accuracy. Amino acid side chain
conformation prediction is essential for protein homology modeling and protein
design. Current widely-adopted methods use physics-based energy functions to
evaluate side chain conformation. Here, using a deep neural network
architecture without physics-based assumptions, we have demonstrated that side
chain conformation prediction accuracy can be improved by more than 25%,
especially for aromatic residues compared with current standard methods. More
strikingly, the prediction method presented here is robust enough to identify
individual conformational outliers from high resolution structures in a protein
data bank without providing its structural factors. We envisage that our amino
acid side chain predictor could be used as a quality check step for future
protein structure model validation and many other potential applications such
as side chain assignment in Cryo-electron microscopy, crystallography model
auto-building, protein folding and small molecule ligand docking.
Updating Singular Value Decomposition for Rank One Matrix Perturbation
Authors: Ratnik Gandhi, Amoli Rajgor
Date: 2017-07-26 00:00:00

An efficient Singular Value Decomposition (SVD) algorithm is an important
tool for distributed and streaming computation in big data problems. It is
observed that update of singular vectors of a rank-1 perturbed matrix is
similar to a Cauchy matrix-vector product. With this observation, in this
paper, we present an efficient method for updating Singular Value Decomposition
of rank-1 perturbed matrix in $O(n^2 \ \text{log}(\frac{1}{\epsilon}))$ time.
The method uses Fast Multipole Method (FMM) for updating singular vectors in
$O(n \ \text{log} (\frac{1}{\epsilon}))$ time, where $\epsilon$ is the
precision of computation.
Deep Interactive Region Segmentation and Captioning
Authors: Ali Sharifi Boroujerdi, Maryam Khanian, Michael Breuss
Date: 2017-07-26 00:00:00
Tasks: Image Captioning, Interactive Segmentation, object-detection, Object Detection, Segmentation

With recent innovations in dense image captioning, it is now possible to
describe every object of the scene with a caption while objects are determined
by bounding boxes. However, interpretation of such an output is not trivial due
to the existence of many overlapping bounding boxes. Furthermore, in current
captioning frameworks, the user is not able to involve personal preferences to
exclude out of interest areas. In this paper, we propose a novel hybrid deep
learning architecture for interactive region segmentation and captioning where
the user is able to specify an arbitrary region of the image that should be
processed. To this end, a dedicated Fully Convolutional Network (FCN) named
Lyncean FCN (LFCN) is trained using our special training data to isolate the
User Intention Region (UIR) as the output of an efficient segmentation. In
parallel, a dense image captioning model is utilized to provide a wide variety
of captions for that region. Then, the UIR will be explained with the caption
of the best match bounding box. To the best of our knowledge, this is the first
work that provides such a comprehensive output. Our experiments show the
superiority of the proposed approach over state-of-the-art interactive
segmentation methods on several well-known datasets. In addition, replacement
of the bounding boxes with the result of the interactive segmentation leads to
a better understanding of the dense image captioning output as well as accuracy
enhancement for the object detection in terms of Intersection over Union (IoU).
General Latent Feature Modeling for Data Exploration Tasks
Authors: Isabel Valera, Melanie F. Pradier, Zoubin Ghahramani
Date: 2017-07-26 00:00:00

This paper introduces a general Bayesian non- parametric latent feature model
suitable to per- form automatic exploratory analysis of heterogeneous datasets,
where the attributes describing each object can be either discrete, continuous
or mixed variables. The proposed model presents several important properties.
First, it accounts for heterogeneous data while can be inferred in linear time
with respect to the number of objects and attributes. Second, its Bayesian
nonparametric nature allows us to automatically infer the model complexity from
the data, i.e., the number of features necessary to capture the latent
structure in the data. Third, the latent features in the model are
binary-valued variables, easing the interpretability of the obtained latent
features in data exploration tasks.
Modelling the Scene Dependent Imaging in Cameras with a Deep Neural Network
Authors: Seonghyeon Nam, Seon Joo Kim
Date: 2017-07-26 00:00:00
Proceeding: ICCV 2017 10
Tasks: Deblurring, Image Deblurring

We present a novel deep learning framework that models the scene dependent
image processing inside cameras. Often called as the radiometric calibration,
the process of recovering RAW images from processed images (JPEG format in the
sRGB color space) is essential for many computer vision tasks that rely on
physically accurate radiance values. All previous works rely on the
deterministic imaging model where the color transformation stays the same
regardless of the scene and thus they can only be applied for images taken
under the manual mode. In this paper, we propose a data-driven approach to
learn the scene dependent and locally varying image processing inside cameras
under the automode. Our method incorporates both the global and the local scene
context into pixel-wise features via multi-scale pyramid of learnable histogram
layers. The results show that we can model the imaging pipeline of different
cameras that operate under the automode accurately in both directions (from RAW
to sRGB, from sRGB to RAW) and we show how we can apply our method to improve
the performance of image deblurring.
RankIQA: Learning from Rankings for No-reference Image Quality Assessment
Authors: Xialei Liu, Joost Van de Weijer, Andrew D. Bagdanov
Date: 2017-07-26 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Quality Assessment, No-Reference Image Quality Assessment

We propose a no-reference image quality assessment (NR-IQA) approach that
learns from rankings (RankIQA). To address the problem of limited IQA dataset
size, we train a Siamese Network to rank images in terms of image quality by
using synthetically generated distortions for which relative image quality is
known. These ranked image sets can be automatically generated without laborious
human labeling. We then use fine-tuning to transfer the knowledge represented
in the trained Siamese Network to a traditional CNN that estimates absolute
image quality from single images. We demonstrate how our approach can be made
significantly more efficient than traditional Siamese Networks by forward
propagating a batch of images through a single network and backpropagating
gradients derived from all pairs of images in the batch. Experiments on the
TID2013 benchmark show that we improve the state-of-the-art by over 5%.
Furthermore, on the LIVE benchmark we show that our approach is superior to
existing NR-IQA techniques and that we even outperform the state-of-the-art in
full-reference IQA (FR-IQA) methods without having to resort to high-quality
reference images to infer IQA.
Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective
Authors: Seong Joon Oh, Mario Fritz, Bernt Schiele
Date: 2017-03-28 00:00:00
Proceeding: ICCV 2017 10
Tasks: Person Recognition

Users like sharing personal photos with others through social media. At the
same time, they might want to make automatic identification in such photos
difficult or even impossible. Classic obfuscation methods such as blurring are
not only unpleasant but also not as effective as one would expect. Recent
studies on adversarial image perturbations (AIP) suggest that it is possible to
confuse recognition systems effectively without unpleasant artifacts. However,
in the presence of counter measures against AIPs, it is unclear how effective
AIP would be in particular when the choice of counter measure is unknown. Game
theory provides tools for studying the interaction between agents with
uncertainties in the strategies. We introduce a general game theoretical
framework for the user-recogniser dynamics, and present a case study that
involves current state of the art AIP and person recognition techniques. We
derive the optimal strategy for the user that assures an upper bound on the
recognition rate independent of the recogniser's counter measure. Code is
available at https://goo.gl/hgvbNK.
Declarative Sequential Pattern Mining of Care Pathways
Authors: Thomas Guyet, André Happe, Yann Dauxais
Date: 2017-07-26 00:00:00
Tasks: Sequential Pattern Mining

Sequential pattern mining algorithms are widely used to explore care pathways
database, but they generate a deluge of patterns, mostly redundant or useless.
Clinicians need tools to express complex mining queries in order to generate
less but more significant patterns. These algorithms are not versatile enough
to answer complex clinician queries. This article proposes to apply a
declarative pattern mining approach based on Answer Set Programming paradigm.
It is exemplified by a pharmaco-epidemiological study investigating the
possible association between hospitalization for seizure and antiepileptic drug
switch from a french medico-administrative database.
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning
Authors: Yukai Shi, Keze Wang, Chongyu Chen, Li Xu, Liang Lin
Date: 2017-07-26 00:00:00
Tasks: Computational Efficiency, Image Restoration, Image Super-Resolution, Multi-Task Learning, Super-Resolution

Single image super resolution (SR), which refers to reconstruct a
higher-resolution (HR) image from the observed low-resolution (LR) image, has
received substantial attention due to its tremendous application potentials.
Despite the breakthroughs of recently proposed SR methods using convolutional
neural networks (CNNs), their generated results usually lack of preserving
structural (high-frequency) details. In this paper, regarding global boundary
context and residual context as complimentary information for enhancing
structural details in image restoration, we develop a contextualized multi-task
learning framework to address the SR problem. Specifically, our method first
extracts convolutional features from the input LR image and applies one
deconvolutional module to interpolate the LR feature maps in a content-adaptive
way. Then, the resulting feature maps are fed into two branched sub-networks.
During the neural network training, one sub-network outputs salient image
boundaries and the HR image, and the other sub-network outputs the local
residual map, i.e., the residual difference between the generated HR image and
ground-truth image. On several standard benchmarks (i.e., Set5, Set14 and
BSD200), our extensive evaluations demonstrate the effectiveness of our SR
method on achieving both higher restoration quality and computational
efficiency compared with several state-of-the-art SR approaches. The source
code and some SR results can be found at:
http://hcp.sysu.edu.cn/structure-preserving-image-super-resolution/
Asymmetric Deep Supervised Hashing
Authors: Qing-Yuan Jiang, Wu-Jun Li
Date: 2017-07-26 00:00:00
Tasks: Retrieval

Hashing has been widely used for large-scale approximate nearest neighbor
search because of its storage and search efficiency. Recent work has found that
deep supervised hashing can significantly outperform non-deep supervised
hashing in many applications. However, most existing deep supervised hashing
methods adopt a symmetric strategy to learn one deep hash function for both
query points and database (retrieval) points. The training of these symmetric
deep supervised hashing methods is typically time-consuming, which makes them
hard to effectively utilize the supervised information for cases with
large-scale database. In this paper, we propose a novel deep supervised hashing
method, called asymmetric deep supervised hashing (ADSH), for large-scale
nearest neighbor search. ADSH treats the query points and database points in an
asymmetric way. More specifically, ADSH learns a deep hash function only for
query points, while the hash codes for database points are directly learned.
The training of ADSH is much more efficient than that of traditional symmetric
deep supervised hashing methods. Experiments show that ADSH can achieve
state-of-the-art performance in real applications.
Learning Sparse Representations in Reinforcement Learning with Sparse Coding
Authors: Lei Le, Raksha Kumaraswamy, Martha White
Date: 2017-07-26 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

A variety of representation learning approaches have been investigated for
reinforcement learning; much less attention, however, has been given to
investigating the utility of sparse coding. Outside of reinforcement learning,
sparse coding representations have been widely used, with non-convex objectives
that result in discriminative representations. In this work, we develop a
supervised sparse coding objective for policy evaluation. Despite the
non-convexity of this objective, we prove that all local minima are global
minima, making the approach amenable to simple optimization strategies. We
empirically show that it is key to use a supervised objective, rather than the
more straightforward unsupervised sparse coding approach. We compare the
learned representations to a canonical fixed sparse representation, called
tile-coding, demonstrating that the sparse coding representation outperforms a
wide variety of tilecoding representations.
Residual Conv-Deconv Grid Network for Semantic Segmentation
Authors: Damien Fourure, Rémi Emonet, Elisa Fromont, Damien Muselet, Alain Tremeau, Christian Wolf
Date: 2017-07-25 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

This paper presents GridNet, a new Convolutional Neural Network (CNN)
architecture for semantic image segmentation (full scene labelling). Classical
neural networks are implemented as one stream from the input to the output with
subsampling operators applied in the stream in order to reduce the feature maps
size and to increase the receptive field for the final prediction. However, for
semantic image segmentation, where the task consists in providing a semantic
class to each pixel of an image, feature maps reduction is harmful because it
leads to a resolution loss in the output prediction. To tackle this problem,
our GridNet follows a grid pattern allowing multiple interconnected streams to
work at different resolutions. We show that our network generalizes many well
known networks such as conv-deconv, residual or U-Net networks. GridNet is
trained from scratch and achieves competitive results on the Cityscapes
dataset.
Stochastic Alternating Direction Method of Multipliers with Variance Reduction for Nonconvex Optimization
Authors: Feihu Huang, Songcan Chen, Zhaosong Lu
Date: 2016-10-10 00:00:00

In the paper, we study the stochastic alternating direction method of
multipliers (ADMM) for the nonconvex optimizations, and propose three classes
of the nonconvex stochastic ADMM with variance reduction, based on different
reduced variance stochastic gradients. Specifically, the first class called the
nonconvex stochastic variance reduced gradient ADMM (SVRG-ADMM), uses a
multi-stage scheme to progressively reduce the variance of stochastic
gradients. The second is the nonconvex stochastic average gradient ADMM
(SAG-ADMM), which additionally uses the old gradients estimated in the previous
iteration. The third called SAGA-ADMM is an extension of the SAG-ADMM method.
Moreover, under some mild conditions, we establish the iteration complexity
bound of $O(1/\epsilon)$ of the proposed methods to obtain an
$\epsilon$-stationary solution of the nonconvex optimizations. In particular,
we provide a general framework to analyze the iteration complexity of these
nonconvex stochastic ADMM methods with variance reduction. Finally, some
numerical experiments demonstrate the effectiveness of our methods.
ASDA : Analyseur Syntaxique du Dialecte Alg{é}rien dans un but d'analyse s{é}mantique
Authors: Imène Guellil, Faiçal Azouaou
Date: 2017-07-26 00:00:00
Tasks: Opinion Mining, POS, POS Tagging, Sentiment Analysis, Translation

Opinion mining and sentiment analysis in social media is a research issue
having a great interest in the scientific community. However, before begin this
analysis, we are faced with a set of problems. In particular, the problem of
the richness of languages and dialects within these media. To address this
problem, we propose in this paper an approach of construction and
implementation of Syntactic analyzer named ASDA. This tool represents a parser
for the Algerian dialect that label the terms of a given corpus. Thus, we
construct a labeling table containing for each term its stem, different
prefixes and suffixes, allowing us to determine the different grammatical parts
a sort of POS tagging. This labeling will serve us later in the semantic
processing of the Algerian dialect, like the automatic translation of this
dialect or sentiment analysis