pwc-archive/papers-with-abstracts huggingface dataset:
A Generative Adversarial Model for Right Ventricle Segmentation
Authors: Nicoló Savioli, Miguel Silva Vieira, Pablo Lamata, Giovanni Montana
Date: 2018-09-27 00:00:00
Tasks: Management, model, Right Ventricle Segmentation

The clinical management of several cardiovascular conditions, such as
pulmonary hypertension, require the assessment of the right ventricular (RV)
function. This work addresses the fully automatic and robust access to one of
the key RV biomarkers, its ejection fraction, from the gold standard imaging
modality, MRI. The problem becomes the accurate segmentation of the RV blood
pool from cine MRI sequences. This work proposes a solution based on Fully
Convolutional Neural Networks (FCNN), where our first contribution is the
optimal combination of three concepts (the convolution Gated Recurrent Units
(GRU), the Generative Adversarial Networks (GAN), and the L1 loss function)
that achieves an improvement of 0.05 and 3.49 mm in Dice Index and Hausdorff
Distance respectively with respect to the baseline FCNN. This improvement is
then doubled by our second contribution, the ROI-GAN, that sets two GANs to
cooperate working at two fields of view of the image, its full resolution and
the region of interest (ROI). Our rationale here is to better guide the FCNN
learning by combining global (full resolution) and local Region Of Interest
(ROI) features. The study is conducted in a large in-house dataset of $\sim$
23.000 segmented MRI slices, and its generality is verified in a publicly
available dataset.
Flow-based Network Traffic Generation using Generative Adversarial Networks
Authors: Markus Ring, Daniel Schlör, Dieter Landes, Andreas Hotho
Date: 2018-09-27 00:00:00
Tasks: Image Generation, Intrusion Detection

Flow-based data sets are necessary for evaluating network-based intrusion
detection systems (NIDS). In this work, we propose a novel methodology for
generating realistic flow-based network traffic. Our approach is based on
Generative Adversarial Networks (GANs) which achieve good results for image
generation. A major challenge lies in the fact that GANs can only process
continuous attributes. However, flow-based data inevitably contain categorical
attributes such as IP addresses or port numbers. Therefore, we propose three
different preprocessing approaches for flow-based data in order to transform
them into continuous values. Further, we present a new method for evaluating
the generated flow-based network traffic which uses domain knowledge to define
quality tests. We use the three approaches for generating flow-based network
traffic based on the CIDDS-001 data set. Experiments indicate that two of the
three approaches are able to generate high quality data.
Finding Mixed Nash Equilibria of Generative Adversarial Networks
Authors: Ya-Ping Hsieh, Chen Liu, Volkan Cevher
Date: 2018-10-23 00:00:00
Proceeding: ICLR 2019 5

We reconsider the training objective of Generative Adversarial Networks
(GANs) from the mixed Nash Equilibria (NE) perspective. Inspired by the
classical prox methods, we develop a novel algorithmic framework for GANs via
an infinite-dimensional two-player game and prove rigorous convergence rates to
the mixed NE, resolving the longstanding problem that no provably convergent
algorithm exists for general GANs. We then propose a principled procedure to
reduce our novel prox methods to simple sampling routines, leading to
practically efficient algorithms. Finally, we provide experimental evidence
that our approach outperforms methods that seek pure strategy equilibria, such
as SGD, Adam, and RMSProp, both in speed and quality.
Computational Histological Staining and Destaining of Prostate Core Biopsy RGB Images with Generative Adversarial Neural Networks
Authors: Aman Rana, Gregory Yauney, Alarice Lowe, Pratik Shah
Date: 2018-10-26 00:00:00
Tasks: Generative Adversarial Network

Histopathology tissue samples are widely available in two states:
paraffin-embedded unstained and non-paraffin-embedded stained whole slide RGB
images (WSRI). Hematoxylin and eosin stain (H&E) is one of the principal stains
in histology but suffers from several shortcomings related to tissue
preparation, staining protocols, slowness and human error. We report two novel
approaches for training machine learning models for the computational H&E
staining and destaining of prostate core biopsy RGB images. The staining model
uses a conditional generative adversarial network that learns hierarchical
non-linear mappings between whole slide RGB image (WSRI) pairs of prostate core
biopsy before and after H&E staining. The trained staining model can then
generate computationally H&E-stained prostate core WSRIs using previously
unseen non-stained biopsy images as input. The destaining model, by learning
mappings between an H&E stained WSRI and a non-stained WSRI of the same biopsy,
can computationally destain previously unseen H&E-stained images. Structural
and anatomical details of prostate tissue and colors, shapes, geometries,
locations of nuclei, stroma, vessels, glands and other cellular components were
generated by both models with structural similarity indices of 0.68 (staining)
and 0.84 (destaining). The proposed staining and destaining models can engender
computational H&E staining and destaining of WSRI biopsies without additional
equipment and devices.
Deep learning framework DNN with conditional WGAN for protein solubility prediction
Authors: X. Han, L. Zhang, K. Zhou, X. Wang
Date: 2018-11-17 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation

Protein solubility plays a critical role in improving production yield of
recombinant proteins in biocatalyst and pharmaceutical field. To some extent,
protein solubility can represent the function and activity of biocatalysts
which are mainly composed of recombinant proteins. Highly soluble proteins are
more effective in biocatalytic processes and can reduce the cost of
biocatalysts. Screening proteins by experiments in vivo is time-consuming and
expensive. In literature, large amounts of machine learning models have been
investigated, whereas parameters of those models are underdetermined with
insufficient data of protein solubility. A data augmentation algorithm that can
enlarge the dataset of protein solubility and improve the performance of
prediction model is highly desired, which can alleviate the common issue of
insufficient data in biotechnology applications for developing machine learning
models. We first implemented a novel approach that a data augmentation
algorithm, conditional WGAN was used to improve prediction performance of DNN
for protein solubility from protein sequence by generating artificial data.
After adding mimic data produced from conditional WGAN, the prediction
performance represented by $R^{2}$ was improved compared with the $R^{2}$
without data augmentation. After tuning the hyperparameters of two algorithms
and organizing the dataset, we achieved a $R^{2}$ value of $45.04\%$, which
enhanced $R^{2}$ about $10\%$ compared with the previous study using the same
dataset. Data augmentation opens the door to applications of machine learning
models on biological data, as machine learning models always fail to be well
trained by small datasets.
GANtruth - an unpaired image-to-image translation method for driving scenarios
Authors: Sebastian Bujwid, Miquel Martí, Hossein Azizpour, Alessandro Pieropan
Date: 2018-11-26 00:00:00
Tasks: Diversity, Domain Adaptation, Image-to-Image Translation, Instance Segmentation, Segmentation, Semantic Segmentation, Translation

Synthetic image translation has significant potentials in autonomous
transportation systems. That is due to the expense of data collection and
annotation as well as the unmanageable diversity of real-words situations. The
main issue with unpaired image-to-image translation is the ill-posed nature of
the problem. In this work, we propose a novel method for constraining the
output space of unpaired image-to-image translation. We make the assumption
that the environment of the source domain is known (e.g. synthetically
generated), and we propose to explicitly enforce preservation of the
ground-truth labels on the translated images.
  We experiment on preserving ground-truth information such as semantic
segmentation, disparity, and instance segmentation. We show significant
evidence that our method achieves improved performance over the
state-of-the-art model of UNIT for translating images from SYNTHIA to
Cityscapes. The generated images are perceived as more realistic in human
surveys and outperforms UNIT when used in a domain adaptation scenario for
semantic segmentation.
Generative Adversarial Network based Speaker Adaptation for High Fidelity WaveNet Vocoder
Authors: Qiao Tian, Bing Yang, Jing Chen, Benlai Tang, Shan Liu
Date: 2018-12-06 00:00:00
Tasks: Generative Adversarial Network, text-to-speech, Text to Speech, Vocal Bursts Intensity Prediction

Neural networks based vocoders, typically the WaveNet, have achieved
spectacular performance for text-to-speech (TTS) in recent years. Although
state-of-the-art parallel WaveNet has addressed the issue of real-time waveform
generation, there remains problems. Firstly, due to the noisy input signal of
the model, there is still a gap between the quality of generated and natural
waveforms. Secondly, a parallel WaveNet is trained under a distilled training
framework, which makes it tedious to adapt a well trained model to a new
speaker. To address these two problems, this paper proposes an end-to-end
adaptation method based on the generative adversarial network (GAN), which can
reduce the computational cost for the training of new speaker adaptation. Our
subjective experiments shows that the proposed training method can further
reduce the quality gap between generated and natural waveforms.
Context-Dependent Diffusion Network for Visual Relationship Detection
Authors: Zhen Cui, Chunyan Xu, Wenming Zheng, Jian Yang
Date: 2018-09-11 00:00:00
Tasks: Diversity, Object, Object Recognition, Relationship Detection, Scene Understanding, Visual Relationship Detection

Visual relationship detection can bridge the gap between computer vision and
natural language for scene understanding of images. Different from pure object
recognition tasks, the relation triplets of subject-predicate-object lie on an
extreme diversity space, such as \textit{person-behind-person} and
\textit{car-behind-building}, while suffering from the problem of combinatorial
explosion. In this paper, we propose a context-dependent diffusion network
(CDDN) framework to deal with visual relationship detection. To capture the
interactions of different object instances, two types of graphs, word semantic
graph and visual scene graph, are constructed to encode global context
interdependency. The semantic graph is built through language priors to model
semantic correlations across objects, whilst the visual scene graph defines the
connections of scene objects so as to utilize the surrounding scene
information. For the graph-structured data, we design a diffusion network to
adaptively aggregate information from contexts, which can effectively learn
latent representations of visual relationships and well cater to visual
relationship detection in view of its isomorphic invariance to graphs.
Experiments on two widely-used datasets demonstrate that our proposed method is
more effective and achieves the state-of-the-art performance.
Densely Supervised Grasp Detector (DSGD)
Authors: Umar Asif, Jianbin Tang, Stefan Harrer
Date: 2018-10-01 00:00:00
Tasks: Region Proposal, Robotic Grasping

This paper presents Densely Supervised Grasp Detector (DSGD), a deep learning
framework which combines CNN structures with layer-wise feature fusion and
produces grasps and their confidence scores at different levels of the image
hierarchy (i.e., global-, region-, and pixel-levels). % Specifically, at the
global-level, DSGD uses the entire image information to predict a grasp. At the
region-level, DSGD uses a region proposal network to identify salient regions
in the image and predicts a grasp for each salient region. At the pixel-level,
DSGD uses a fully convolutional network and predicts a grasp and its confidence
at every pixel. % During inference, DSGD selects the most confident grasp as
the output. This selection from hierarchically generated grasp candidates
overcomes limitations of the individual models. % DSGD outperforms
state-of-the-art methods on the Cornell grasp dataset in terms of grasp
accuracy. % Evaluation on a multi-object dataset and real-world robotic
grasping experiments show that DSGD produces highly stable grasps on a set of
unseen objects in new environments. It achieves 97% grasp detection accuracy
and 90% robotic grasping success rate with real-time inference speed.
Method for estimating modulation transfer function from sample images
Authors: Rino Saiga, Akihisa Takeuchi, Kentaro Uesugi, Yasuko Terada, Yoshio Suzuki, Ryuta Mizutani
Date: 2017-11-24 00:00:00

The modulation transfer function (MTF) represents the frequency domain
response of imaging modalities. Here, we report a method for estimating the MTF
from sample images. Test images were generated from a number of images,
including those taken with an electron microscope and with an observation
satellite. These original images were convolved with point spread functions
(PSFs) including those of circular apertures. The resultant test images were
subjected to a Fourier transformation. The logarithm of the squared norm of the
Fourier transform was plotted against the squared distance from the origin.
Linear correlations were observed in the logarithmic plots, indicating that the
PSF of the test images can be approximated with a Gaussian. The MTF was then
calculated from the Gaussian-approximated PSF. The obtained MTF closely
coincided with the MTF predicted from the original PSF. The MTF of an x-ray
microtomographic section of a fly brain was also estimated with this method.
The obtained MTF showed good agreement with the MTF determined from an edge
profile of an aluminum test object. We suggest that this approach is an
alternative way of estimating the MTF, independently of the image type.
Algorithms for nonnegative matrix factorization with the beta-divergence
Authors: Cédric Févotte, Jérôme Idier
Date: 2010-10-08 00:00:00

This paper describes algorithms for nonnegative matrix factorization (NMF)
with the beta-divergence (beta-NMF). The beta-divergence is a family of cost
functions parametrized by a single shape parameter beta that takes the
Euclidean distance, the Kullback-Leibler divergence and the Itakura-Saito
divergence as special cases (beta = 2,1,0, respectively). The proposed
algorithms are based on a surrogate auxiliary function (a local majorization of
the criterion function). We first describe a majorization-minimization (MM)
algorithm that leads to multiplicative updates, which differ from standard
heuristic multiplicative updates by a beta-dependent power exponent. The
monotonicity of the heuristic algorithm can however be proven for beta in (0,1)
using the proposed auxiliary function. Then we introduce the concept of
majorization-equalization (ME) algorithm which produces updates that move along
constant level sets of the auxiliary function and lead to larger steps than MM.
Simulations on synthetic and real data illustrate the faster convergence of the
ME approach. The paper also describes how the proposed algorithms can be
adapted to two common variants of NMF : penalized NMF (i.e., when a penalty
function of the factors is added to the criterion function) and convex-NMF
(when the dictionary is assumed to belong to a known subspace).
Transfer Learning Using Logistic Regression in Credit Scoring
Authors: Farid Beninel, Waad Bouaguel, Ghazi Belmufti
Date: 2012-12-26 00:00:00
Tasks: Management, regression, Transfer Learning

The credit scoring risk management is a fast growing field due to consumer's
credit requests. Credit requests, of new and existing customers, are often
evaluated by classical discrimination rules based on customers information.
However, these kinds of strategies have serious limits and don't take into
account the characteristics difference between current customers and the future
ones. The aim of this paper is to measure credit worthiness for non customers
borrowers and to model potential risk given a heterogeneous population formed
by borrowers customers of the bank and others who are not. We hold on previous
works done in generalized gaussian discrimination and transpose them into the
logistic model to bring out efficient discrimination rules for non customers'
subpopulation.
  Therefore we obtain several simple models of connection between parameters of
both logistic models associated respectively to the two subpopulations. The
German credit data set is selected to experiment and to compare these models.
Experimental results show that the use of links between the two subpopulations
improve the classification accuracy for the new loan applicants.
PyText: A Seamless Path from NLP research to production
Authors: Ahmed Aly, Kushal Lakhotia, Shicong Zhao, Mrinal Mohit, Barlas Oguz, Abhinav Arora, Sonal Gupta, Christopher Dewan, Stef Nelson-Lindall, Rushin Shah
Date: 2018-12-12 00:00:00

We introduce PyText - a deep learning based NLP modeling framework built on
PyTorch. PyText addresses the often-conflicting requirements of enabling rapid
experimentation and of serving models at scale. It achieves this by providing
simple and extensible interfaces for model components, and by using PyTorch's
capabilities of exporting models for inference via the optimized Caffe2
execution engine. We report our own experience of migrating experimentation and
production workflows to PyText, which enabled us to iterate faster on novel
modeling ideas and then seamlessly ship them at industrial scale.
Cluster-Based Learning from Weakly Labeled Bags in Digital Pathology
Authors: Shazia Akbar, Anne L. Martel
Date: 2018-11-28 00:00:00
Tasks: Weakly-supervised Learning

To alleviate the burden of gathering detailed expert annotations when
training deep neural networks, we propose a weakly supervised learning approach
to recognize metastases in microscopic images of breast lymph nodes. We
describe an alternative training loss which clusters weakly labeled bags in
latent space to inform relevance of patch-instances during training of a
convolutional neural network. We evaluate our method on the Camelyon dataset
which contains high-resolution digital slides of breast lymph nodes, where
labels are provided at the image-level and only subsets of patches are made
available during training.
High resolution neural connectivity from incomplete tracing data using nonnegative spline regression
Authors: Kameron Decker Harris, Stefan Mihalas, Eric Shea-Brown
Date: 2016-05-24 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Matrix Completion, regression

Whole-brain neural connectivity data are now available from viral tracing
experiments, which reveal the connections between a source injection site and
elsewhere in the brain. These hold the promise of revealing spatial patterns of
connectivity throughout the mammalian brain. To achieve this goal, we seek to
fit a weighted, nonnegative adjacency matrix among 100 $\mu$m brain "voxels"
using viral tracer data. Despite a multi-year experimental effort, injections
provide incomplete coverage, and the number of voxels in our data is orders of
magnitude larger than the number of injections, making the problem severely
underdetermined. Furthermore, projection data are missing within the injection
site because local connections there are not separable from the injection
signal.
  We use a novel machine-learning algorithm to meet these challenges and
develop a spatially explicit, voxel-scale connectivity map of the mouse visual
system. Our method combines three features: a matrix completion loss for
missing data, a smoothing spline penalty to regularize the problem, and
(optionally) a low rank factorization. We demonstrate the consistency of our
estimator using synthetic data and then apply it to newly available Allen Mouse
Brain Connectivity Atlas data for the visual system. Our algorithm is
significantly more predictive than current state of the art approaches which
assume regions to be homogeneous. We demonstrate the efficacy of a low rank
version on visual cortex data and discuss the possibility of extending this to
a whole-brain connectivity matrix at the voxel scale.
Efficient Power Spectrum Estimation for High Resolution CMB Maps
Authors: Sudeep Das, Amir Hajian, David N. Spergel
Date: 2008-09-05 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Estimation of the angular power spectrum of the Cosmic Microwave Background
(CMB) on a small patch of sky is usually plagued by serious spectral leakage,
specially when the map has a hard edge. Even on a full sky map, point source
masks can alias power from large scales to small scales producing excess
variance at high multipoles. We describe a new fast, simple and local method
for estimation of power spectra on small patches of the sky that minimizes
spectral leakage and reduces the variance of the spectral estimate. For
example, when compared with the standard uniform sampling approach on a 8
degree X 8 degree patch of the sky with 2% area masked due to point sources,
our estimator halves the errorbars at l=2000 and achieves a more than fourfold
reduction in errorbars at l=3500. Thus, a properly analyzed experiment will
have errorbars at l=3500 equivalent to those of an experiment analyzed with the
now standard technique with ~ 16-25 times the integration time.
Geometry of the restricted Boltzmann machine
Authors: Maria Angelica Cueto, Jason Morton, Bernd Sturmfels
Date: 2009-08-30 00:00:00

The restricted Boltzmann machine is a graphical model for binary random
variables. Based on a complete bipartite graph separating hidden and observed
variables, it is the binary analog to the factor analysis model. We study this
graphical model from the perspectives of algebraic statistics and tropical
geometry, starting with the observation that its Zariski closure is a Hadamard
power of the first secant variety of the Segre variety of projective lines. We
derive a dimension formula for the tropicalized model, and we use it to show
that the restricted Boltzmann machine is identifiable in many cases. Our
methods include coding theory and geometry of linear threshold functions.
Apple Machine Learning Algorithms Successfully Detect Colon Cancer but Fail to Predict KRAS Mutation Status
Authors: Andrew A. Borkowski, Catherine P. Wilson, Steven A. Borkowski, L. Brannon Thomas, Lauren A. Deland, Stephen M. Mastorides
Date: 2018-12-11 00:00:00
Tasks: Diagnostic, Prognosis

Colon cancer is the second leading cause of cancer-related death in the
United States of America. Its prognosis has significantly improved with the
advancement of targeted therapies based on underlying molecular changes. The
KRAS mutation is one of the most frequent molecular alterations seen in colon
cancer and its presence can affect treatment selection. We attempted to use
Apple machine learning algorithms to diagnose colon cancer and predict the KRAS
mutation status from histopathological images. We captured 250 colon cancer
images and 250 benign colon tissue images. Half of colon cancer images were
captured from KRAS mutation-positive tumors and another half from KRAS
mutation-negative tumors. Next, we created Image Classifier Model using Apple
CreateML machine learning module. The trained and validated model was able to
successfully differentiate between colon cancer and benign colon tissue images
with 98 % accuracy and 98 % precision. However, our model failed to reliably
identify KRAS mutations, with the highest realized accuracy of 66 %. Although
not yet perfected, in the near future Apple CreateML modules can be used in
diagnostic smartphone-based applications and potentially alleviate shortages of
medical professionals in understaffed parts of the world.