pwc-archive/papers-with-abstracts huggingface dataset:
Exploitation of Semantic Keywords for Malicious Event Classification
Authors: Hyungtae Lee, Sungmin Eum, Joel Levis, Heesung Kwon, James Michaelis, Michael Kolodny
Date: 2016-10-21 00:00:00
Tasks: Classification, General Classification

Learning an event classifier is challenging when the scenes are semantically
different but visually similar. However, as humans, we typically handle such
tasks painlessly by adding our background semantic knowledge. Motivated by this
observation, we aim to provide an empirical study about how additional
information such as semantic keywords can boost up the discrimination of such
events. To demonstrate the validity of this study, we first construct a novel
Malicious Crowd Dataset containing crowd images with two events, benign and
malicious, which look visually similar. Note that the primary focus of this
paper is not to provide the state-of-the-art performance on this dataset but to
show the beneficial aspects of using semantically-driven keyword information.
By leveraging crowd-sourcing platforms, such as Amazon Mechanical Turk, we
collect semantic keywords associated with images and then subsequently identify
a subset of keywords (e.g. police, fire, etc.) unique to specific events. We
first show that by using recently introduced attention models, a naive
CNN-based event classifier actually learns to primarily focus on local
attributes associated with the discriminant semantic keywords identified by the
Turks. We further show that incorporating the keyword-driven information into
early- and late-fusion approaches can significantly enhance malicious event
classification.
Unsupervised Generative Adversarial Cross-modal Hashing
Authors: Jian Zhang, Yuxin Peng, Mingkuan Yuan
Date: 2017-12-01 00:00:00
Tasks: Cross-Modal Retrieval, Generative Adversarial Network, Representation Learning, Retrieval

Cross-modal hashing aims to map heterogeneous multimedia data into a common
Hamming space, which can realize fast and flexible retrieval across different
modalities. Unsupervised cross-modal hashing is more flexible and applicable
than supervised methods, since no intensive labeling work is involved. However,
existing unsupervised methods learn hashing functions by preserving inter and
intra correlations, while ignoring the underlying manifold structure across
different modalities, which is extremely helpful to capture meaningful nearest
neighbors of different modalities for cross-modal retrieval. To address the
above problem, in this paper we propose an Unsupervised Generative Adversarial
Cross-modal Hashing approach (UGACH), which makes full use of GAN's ability for
unsupervised representation learning to exploit the underlying manifold
structure of cross-modal data. The main contributions can be summarized as
follows: (1) We propose a generative adversarial network to model cross-modal
hashing in an unsupervised fashion. In the proposed UGACH, given a data of one
modality, the generative model tries to fit the distribution over the manifold
structure, and select informative data of another modality to challenge the
discriminative model. The discriminative model learns to distinguish the
generated data and the true positive data sampled from correlation graph to
achieve better retrieval accuracy. These two models are trained in an
adversarial way to improve each other and promote hashing function learning.
(2) We propose a correlation graph based approach to capture the underlying
manifold structure across different modalities, so that data of different
modalities but within the same manifold can have smaller Hamming distance and
promote retrieval accuracy. Extensive experiments compared with 6
state-of-the-art methods verify the effectiveness of our proposed approach.
ME R-CNN: Multi-Expert R-CNN for Object Detection
Authors: Hyungtae Lee, Sungmin Eum, Heesung Kwon
Date: 2017-04-04 00:00:00
Tasks: object-detection, Object Detection

We introduce Multi-Expert Region-based Convolutional Neural Network (ME R-CNN) which is equipped with multiple experts (ME) where each expert is learned to process a certain type of regions of interest (RoIs). This architecture better captures the appearance variations of the RoIs caused by different shapes, poses, and viewing angles. In order to direct each RoI to the appropriate expert, we devise a novel "learnable" network, which we call, expert assignment network (EAN). EAN automatically learns the optimal RoI-expert relationship even without any supervision of expert assignment. As the major components of ME R-CNN, ME and EAN, are mutually affecting each other while tied to a shared network, neither an alternating nor a naive end-to-end optimization is likely to fail. To address this problem, we introduce a practical training strategy which is tailored to optimize ME, EAN, and the shared network in an end-to-end fashion. We show that both of the architectures provide considerable performance increase over the baselines on PASCAL VOC 07, 12, and MS COCO datasets.
Trans-allelic model for prediction of peptide:MHC-II interactions
Authors: A. M. Degoot, Faraimunashe Chirove, Wilfred Ndifon
Date: 2017-12-01 00:00:00
Tasks: Prediction

Major histocompatibility complex class two (MHC-II) molecules are
trans-membrane proteins and key components of the cellular immune system. Upon
recognition of foreign peptides expressed on the MHC-II binding groove, helper
T cells mount an immune response against invading pathogens. Therefore,
mechanistic identification and knowledge of physico-chemical features that
govern interactions between peptides and MHC-II molecules is useful for the
design of effective epitope-based vaccines, as well as for understanding of
immune responses. In this paper, we present a comprehensive trans-allelic
prediction model, a generalized version of our previous biophysical model, that
can predict peptide interactions for all three human MHC-II loci (HLA-DR,
HLA-DP and HLA-DQ), using both peptide sequence data and structural information
of MHC-II molecules. The advantage of this approach over other machine learning
models is that it offers a simple and plausible physical explanation for
peptide-MHC-II interactions. We train the model using a benchmark experimental
dataset, and measure its predictive performance using novel data. Despite its
relative simplicity, we find that the model has comparable performance to the
state-of-the-art method. Focusing on the physical bases of peptide-MHC binding,
we find support for previous theoretical predictions about the contributions of
certain binding pockets to the binding energy. Additionally, we find that
binding pockets P 4 and P 5 of HLA-DP, which were not previously considered as
primary anchors, do make strong contributions to the binding energy. Together,
the results indicate that our model can serve as a useful complement to
alternative approaches to predicting peptide-MHC interactions.
Radially-Distorted Conjugate Translations
Authors: James Pritts, Zuzana Kukelova, Viktor Larsson, Ondrej Chum
Date: 2017-11-30 00:00:00
Proceeding: CVPR 2018 6

This paper introduces the first minimal solvers that jointly solve for
affine-rectification and radial lens distortion from coplanar repeated
patterns. Even with imagery from moderately distorted lenses, plane
rectification using the pinhole camera model is inaccurate or invalid. The
proposed solvers incorporate lens distortion into the camera model and extend
accurate rectification to wide-angle imagery, which is now common from consumer
cameras. The solvers are derived from constraints induced by the conjugate
translations of an imaged scene plane, which are integrated with the division
model for radial lens distortion. The hidden-variable trick with ideal
saturation is used to reformulate the constraints so that the solvers generated
by the Grobner-basis method are stable, small and fast.
  Rectification and lens distortion are recovered from either one conjugately
translated affine-covariant feature or two independently translated
similarity-covariant features. The proposed solvers are used in a \RANSAC-based
estimator, which gives accurate rectifications after few iterations. The
proposed solvers are evaluated against the state-of-the-art and demonstrate
significantly better rectifications on noisy measurements. Qualitative results
on diverse imagery demonstrate high-accuracy undistortions and rectifications.
The source code is publicly available at https://github.com/prittjam/repeats.
Bayesian inference for spatio-temporal spike-and-slab priors
Authors: Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen
Date: 2015-09-15 00:00:00
Tasks: Bayesian Inference

In this work, we address the problem of solving a series of underdetermined
linear inverse problems subject to a sparsity constraint. We generalize the
spike-and-slab prior distribution to encode a priori correlation of the support
of the solution in both space and time by imposing a transformed Gaussian
process on the spike-and-slab probabilities. An expectation propagation (EP)
algorithm for posterior inference under the proposed model is derived. For
large scale problems, the standard EP algorithm can be prohibitively slow. We
therefore introduce three different approximation schemes to reduce the
computational complexity. Finally, we demonstrate the proposed model using
numerical experiments based on both synthetic and real data sets.
Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation
Authors: Chao Wang, Haiyong Zheng, Zhibin Yu, Ziqiang Zheng, Zhaorui Gu, Bing Zheng
Date: 2017-11-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Inpainting, Image-to-Image Translation, Region Proposal, Translation

Image-to-image translation has been made much progress with embracing
Generative Adversarial Networks (GANs). However, it's still very challenging
for translation tasks that require high quality, especially at high-resolution
and photorealism. In this paper, we present Discriminative Region Proposal
Adversarial Networks (DRPAN) for high-quality image-to-image translation. We
decompose the procedure of image-to-image translation task into three iterated
steps, first is to generate an image with global structure but some local
artifacts (via GAN), second is using our DRPnet to propose the most fake region
from the generated image, and third is to implement "image inpainting" on the
most fake region for more realistic result through a reviser, so that the
system (DRPAN) can be gradually optimized to synthesize images with more
attention on the most artifact local part. Experiments on a variety of
image-to-image translation tasks and datasets validate that our method
outperforms state-of-the-arts for producing high-quality translation results in
terms of both human perceptual studies and automatic quantitative measures.
Prior and Likelihood Choices for Bayesian Matrix Factorisation on Small Datasets
Authors: Thomas Brouwer, Pietro Lio'
Date: 2017-12-01 00:00:00
Tasks: Model Selection

In this paper, we study the effects of different prior and likelihood choices
for Bayesian matrix factorisation, focusing on small datasets. These choices
can greatly influence the predictive performance of the methods. We identify
four groups of approaches: Gaussian-likelihood with real-valued priors,
nonnegative priors, semi-nonnegative models, and finally Poisson-likelihood
approaches. For each group we review several models from the literature,
considering sixteen in total, and discuss the relations between different
priors and matrix norms. We extensively compare these methods on eight
real-world datasets across three application areas, giving both inter- and
intra-group comparisons. We measure convergence runtime speed, cross-validation
performance, sparse and noisy prediction performance, and model selection
robustness. We offer several insights into the trade-offs between prior and
likelihood choices for Bayesian matrix factorisation on small datasets - such
as that Poisson models give poor predictions, and that nonnegative models are
more constrained than real-valued ones.
Neural Signatures for Licence Plate Re-identification
Authors: Abhinav Kumar, Shantanu Gupta, Vladimir Kozitsky, Sriganesh Madhvanath
Date: 2017-12-01 00:00:00
Tasks: Face Recognition, Image Retrieval, Retrieval, Triplet

The problem of vehicle licence plate re-identification is generally
considered as a one-shot image retrieval problem. The objective of this task is
to learn a feature representation (called a "signature") for licence plates.
Incoming licence plate images are converted to signatures and matched to a
previously collected template database through a distance measure. Then, the
input image is recognized as the template whose signature is "nearest" to the
input signature. The template database is restricted to contain only a single
signature per unique licence plate for our problem.
  We measure the performance of deep convolutional net-based features adapted
from face recognition on this task. In addition, we also test a hybrid approach
combining the Fisher vector with a neural network-based embedding called "f2nn"
trained with the Triplet loss function. We find that the hybrid approach
performs comparably while providing computational benefits. The signature
generated by the hybrid approach also shows higher generalizability to datasets
more dissimilar to the training corpus.
A Novel Brain Decoding Method: a Correlation Network Framework for Revealing Brain Connections
Authors: Siyu Yu, Nanning Zheng, Yongqiang Ma, Hao Wu, Badong Chen
Date: 2017-12-01 00:00:00
Tasks: Brain Decoding, Functional Connectivity

Brain decoding is a hot spot in cognitive science, which focuses on
reconstructing perceptual images from brain activities. Analyzing the
correlations of collected data from human brain activities and representing
activity patterns are two problems in brain decoding based on functional
magnetic resonance imaging (fMRI) signals. However, existing correlation
analysis methods mainly focus on the strength information of voxel, which
reveals functional connectivity in the cerebral cortex. They tend to neglect
the structural information that implies the intracortical or intrinsic
connections; that is, structural connectivity. Hence, the effective
connectivity inferred by these methods is relatively unilateral. Therefore, we
proposed a correlation network (CorrNet) framework that could be flexibly
combined with diverse pattern representation models. In the CorrNet framework,
the topological correlation was introduced to reveal structural information.
Rich correlations were obtained, which contributed to specifying the underlying
effective connectivity. We also combined the CorrNet framework with a linear
support vector machine (SVM) and a dynamic evolving spike neuron network (SNN)
for pattern representation separately, thus providing a novel method for
decoding cognitive activity patterns. Experimental results verified the
reliability and robustness of our CorrNet framework and demonstrated that the
new method achieved significant improvement in brain decoding over comparable
methods.
GANosaic: Mosaic Creation with Generative Texture Manifolds
Authors: Nikolay Jetchev, Urs Bergmann, Calvin Seward
Date: 2017-12-01 00:00:00
Tasks: MORPH, Texture Synthesis

This paper presents a novel framework for generating texture mosaics with
convolutional neural networks. Our method is called GANosaic and performs
optimization in the latent noise space of a generative texture model, which
allows the transformation of a content image into a mosaic exhibiting the
visual properties of the underlying texture manifold. To represent that
manifold, we use a state-of-the-art generative adversarial method for texture
synthesis, which can learn expressive texture representations from data and
produce mosaic images with very high resolution. This fully convolutional model
generates smooth (without any visible borders) mosaic images which morph and
blend different textures locally. In addition, we develop a new type of
differentiable statistical regularization appropriate for optimization over the
prior noise space of the PSGAN model.
Utilizing Domain Knowledge in End-to-End Audio Processing
Authors: Tycho Max Sylvester Tax, Jose Luis Diez Antich, Hendrik Purwins, Lars Maaløe
Date: 2017-12-01 00:00:00
Tasks: Environmental Sound Classification, General Classification, Sound Classification

End-to-end neural network based approaches to audio modelling are generally
outperformed by models trained on high-level data representations. In this
paper we present preliminary work that shows the feasibility of training the
first layers of a deep convolutional neural network (CNN) model to learn the
commonly-used log-scaled mel-spectrogram transformation. Secondly, we
demonstrate that upon initializing the first layers of an end-to-end CNN
classifier with the learned transformation, convergence and performance on the
ESC-50 environmental sound classification dataset are similar to a CNN-based
model trained on the highly pre-processed log-scaled mel-spectrogram features.
Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks
Authors: Thanh Hai Nguyen, Yann Chevaleyre, Edi Prifti, Nataliya Sokolovska, Jean-Daniel Zucker
Date: 2017-12-01 00:00:00

Deep learning (DL) techniques have had unprecedented success when applied to
images, waveforms, and texts to cite a few. In general, when the sample size
(N) is much greater than the number of features (d), DL outperforms previous
machine learning (ML) techniques, often through the use of convolution neural
networks (CNNs). However, in many bioinformatics ML tasks, we encounter the
opposite situation where d is greater than N. In these situations, applying DL
techniques (such as feed-forward networks) would lead to severe overfitting.
Thus, sparse ML techniques (such as LASSO e.g.) usually yield the best results
on these tasks. In this paper, we show how to apply CNNs on data which do not
have originally an image structure (in particular on metagenomic data). Our
first contribution is to show how to map metagenomic data in a meaningful way
to 1D or 2D images. Based on this representation, we then apply a CNN, with the
aim of predicting various diseases. The proposed approach is applied on six
different datasets including in total over 1000 samples from various diseases.
This approach could be a promising one for prediction tasks in the
bioinformatics field.
Shape Complementarity Analysis for Objects of Arbitrary Shape
Authors: Morad Behandish, Horea T. Ilies
Date: 2017-12-01 00:00:00
Tasks: Drug Design, global-optimization, Motion Planning

The basic problem of shape complementarity analysis appears fundamental to
applications as diverse as mechanical design, assembly automation, robot motion
planning, micro- and nano-fabrication, protein-ligand binding, and rational
drug design. However, the current challenge lies in the lack of a general
mathematical formulation that applies to objects of arbitrary shape. We propose
that a measure of shape complementarity can be obtained from the extent of
approximate overlap between shape skeletons. A space-continuous implicit
generalization of the skeleton, called the skeletal density function (SDF) is
defined over the Euclidean space that contains the individual assembly
partners. The SDF shape descriptors capture the essential features that are
relevant to proper contact alignment, and are considerably more robust than the
conventional explicit skeletal representations. We express the shape
complementarity score as a convolution of the individual SDFs. The problem then
breaks down to a global optimization of the score over the configuration space
of spatial relations, which can be efficiently implemented using fast Fourier
transforms (FFTs) on nonequispaced samples. We demonstrate the effectiveness of
the scoring approach for several examples from 2D peg-in-hole alignment to more
complex 3D examples in mechanical assembly and protein docking. We show that
the proposed method is reliable, inherently robust against small perturbations,
and effective in steering gradient-based optimization.
Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks
Authors: Tahsin Reasat, Celia Shahnaz
Date: 2017-10-03 00:00:00
Tasks: Electrocardiography (ECG), Specificity

Myocardial Infarction is one of the leading causes of death worldwide. This
paper presents a Convolutional Neural Network (CNN) architecture which takes
raw Electrocardiography (ECG) signal from lead II, III and AVF and
differentiates between inferior myocardial infarction (IMI) and healthy
signals. The performance of the model is evaluated on IMI and healthy signals
obtained from Physikalisch-Technische Bundesanstalt (PTB) database. A
subject-oriented approach is taken to comprehend the generalization capability
of the model and compared with the current state of the art. In a
subject-oriented approach, the network is tested on one patient and trained on
rest of the patients. Our model achieved a superior metrics scores (accuracy=
84.54%, sensitivity= 85.33% and specificity= 84.09%) when compared to the
benchmark. We also analyzed the discriminating strength of the features
extracted by the convolutional layers by means of geometric separability index
and euclidean distance and compared it with the benchmark model.
Optimal Algorithms for Distributed Optimization
Authors: César A. Uribe, Soomin Lee, Alexander Gasnikov, Angelia Nedić
Date: 2017-12-01 00:00:00
Tasks: Distributed Optimization

In this paper, we study the optimal convergence rate for distributed convex
optimization problems in networks. We model the communication restrictions
imposed by the network as a set of affine constraints and provide optimal
complexity bounds for four different setups, namely: the function $F(\xb)
\triangleq \sum_{i=1}^{m}f_i(\xb)$ is strongly convex and smooth, either
strongly convex or smooth or just convex. Our results show that Nesterov's
accelerated gradient descent on the dual problem can be executed in a
distributed manner and obtains the same optimal rates as in the centralized
version of the problem (up to constant or logarithmic factors) with an
additional cost related to the spectral gap of the interaction matrix. Finally,
we discuss some extensions to the proposed setup such as proximal friendly
functions, time-varying graphs, improvement of the condition numbers.
Predicting Adolescent Suicide Attempts with Neural Networks
Authors: Harish S. Bhat, Sidra J. Goldman-Mellor
Date: 2017-11-28 00:00:00
Tasks: Binary Classification, Specificity

Though suicide is a major public health problem in the US, machine learning
methods are not commonly used to predict an individual's risk of
attempting/committing suicide. In the present work, starting with an anonymized
collection of electronic health records for 522,056 unique, California-resident
adolescents, we develop neural network models to predict suicide attempts. We
frame the problem as a binary classification problem in which we use a
patient's data from 2006-2009 to predict either the presence (1) or absence (0)
of a suicide attempt in 2010. After addressing issues such as severely
imbalanced classes and the variable length of a patient's history, we build
neural networks with depths varying from two to eight hidden layers. For test
set observations where we have at least five ED/hospital visits' worth of data
on a patient, our depth-4 model achieves a sensitivity of 0.703, specificity of
0.980, and AUC of 0.958.