pwc-archive/papers-with-abstracts huggingface dataset:
Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts
Authors: Nabeel Abdur Rehman, Maxwell Matthaios Aliapoulios, Disha Umarwani, Rumi Chunara
Date: 2018-06-22 00:00:00
Tasks: Domain Adaptation, Transfer Learning

Acute respiratory infections have epidemic and pandemic potential and thus
are being studied worldwide, albeit in many different contexts and study
formats. Predicting infection from symptom data is critical, though using
symptom data from varied studies in aggregate is challenging because the data
is collected in different ways. Accordingly, different symptom profiles could
be more predictive in certain studies, or even symptoms of the same name could
have different meanings in different contexts. We assess state-of-the-art
transfer learning methods for improving prediction of infection from symptom
data in multiple types of health care data ranging from clinical, to home-visit
as well as crowdsourced studies. We show interesting characteristics regarding
six different study types and their feature domains. Further, we demonstrate
that it is possible to use data collected from one study to predict infection
in another, at close to or better than using a single dataset for prediction on
itself. We also investigate in which conditions specific transfer learning and
domain adaptation methods may perform better on symptom data. This work has the
potential for broad applicability as we show how it is possible to transfer
learning from one public health study design to another, and data collected
from one study may be used for prediction of labels for another, even collected
through different study designs, populations and contexts.
Smart Inverter Grid Probing for Learning Loads: Part I - Identifiability Analysis
Authors: Siddharth Bhela, Vassilis Kekatos, Sriharsha Veeramachaneni
Date: 2018-06-22 00:00:00

Distribution grids currently lack comprehensive real-time metering.
Nevertheless, grid operators require precise knowledge of loads and renewable
generation to accomplish any feeder optimization task. At the same time, new
grid technologies, such as solar photovoltaics and energy storage units are
interfaced via inverters with advanced sensing and actuation capabilities. In
this context, this two-part work puts forth the idea of engaging power
electronics to probe an electric grid and record its voltage response at
actuated and metered buses, to infer non-metered loads. Probing can be
accomplished by commanding inverters to momentarily perturb their power
injections. Multiple probing actions can be induced within a few tens of
seconds. In Part I, load inference via grid probing is formulated as an
implicit nonlinear system identification task, which is shown to be
topologically observable under certain conditions. The conditions can be
readily checked upon solving a max-flow problem on a bipartite graph derived
from the feeder topology and the placement of actuated and non-metered buses.
The analysis holds for single- and multi-phase grids, radial or meshed, and
applies to phasor or magnitude-only voltage data. The topological observability
of distribution systems using smart meter or phasor data is cast and analyzed a
special case.
Diffusion Scattering Transforms on Graphs
Authors: Fernando Gama, Alejandro Ribeiro, Joan Bruna
Date: 2018-06-22 00:00:00
Proceeding: ICLR 2019 5

Stability is a key aspect of data analysis. In many applications, the natural
notion of stability is geometric, as illustrated for example in computer
vision. Scattering transforms construct deep convolutional representations
which are certified stable to input deformations. This stability to
deformations can be interpreted as stability with respect to changes in the
metric structure of the domain. In this work, we show that scattering
transforms can be generalized to non-Euclidean domains using diffusion
wavelets, while preserving a notion of stability with respect to metric changes
in the domain, measured with diffusion maps. The resulting representation is
stable to metric perturbations of the domain while being able to capture
"high-frequency" information, akin to the Euclidean Scattering.
Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen
Authors: Benjamin Q. Huynh, Sanjay Basu
Date: 2018-06-22 00:00:00
Tasks: Humanitarian

Armed conflict has led to an unprecedented number of internally displaced
persons (IDPs) - individuals who are forced out of their homes but remain
within their country. IDPs often urgently require shelter, food, and
healthcare, yet prediction of when large fluxes of IDPs will cross into an area
remains a major challenge for aid delivery organizations. Accurate forecasting
of IDP migration would empower humanitarian aid groups to more effectively
allocate resources during conflicts. We show that monthly flow of IDPs from
province to province in both Syria and Yemen can be accurately forecasted one
month in advance, using publicly available data. We model monthly IDP flow
using data on food price, fuel price, wage, geospatial, and news data. We find
that machine learning approaches can more accurately forecast migration trends
than baseline persistence models. Our findings thus potentially enable
proactive aid allocation for IDPs in anticipation of forecasted arrivals.
Augmented Reality-based Feedback for Technician-in-the-loop C-arm Repositioning
Authors: Mathias Unberath, Javad Fotouhi, Jonas Hajek, Andreas Maier, Greg Osgood, Russell Taylor, Mehran Armand, Nassir Navab
Date: 2018-06-22 00:00:00
Tasks: Anatomy

Interventional C-arm imaging is crucial to percutaneous orthopedic procedures
as it enables the surgeon to monitor the progress of surgery on the anatomy
level. Minimally invasive interventions require repeated acquisition of X-ray
images from different anatomical views to verify tool placement. Achieving and
reproducing these views often comes at the cost of increased surgical time and
radiation dose to both patient and staff. This work proposes a marker-free
"technician-in-the-loop" Augmented Reality (AR) solution for C-arm
repositioning. The X-ray technician operating the C-arm interventionally is
equipped with a head-mounted display capable of recording desired C-arm poses
in 3D via an integrated infrared sensor. For C-arm repositioning to a
particular target view, the recorded C-arm pose is restored as a virtual object
and visualized in an AR environment, serving as a perceptual reference for the
technician. We conduct experiments in a setting simulating orthopedic trauma
surgery. Our proof-of-principle findings indicate that the proposed system can
decrease the 2.76 X-ray images required per desired view down to zero,
suggesting substantial reductions of radiation dose during C-arm repositioning.
The proposed AR solution is a first step towards facilitating communication
between the surgeon and the surgical staff, improving the quality of surgical
image acquisition, and enabling context-aware guidance for surgery rooms of the
future. The concept of technician-in-the-loop design will become relevant to
various interventions considering the expected advancements of sensing and
wearable computing in the near future.
PCA of high dimensional random walks with comparison to neural network training
Authors: Joseph M. Antognini, Jascha Sohl-Dickstein
Date: 2018-06-22 00:00:00
Proceeding: NeurIPS 2018 12

One technique to visualize the training of neural networks is to perform PCA
on the parameters over the course of training and to project to the subspace
spanned by the first few PCA components. In this paper we compare this
technique to the PCA of a high dimensional random walk. We compute the
eigenvalues and eigenvectors of the covariance of the trajectory and prove that
in the long trajectory and high dimensional limit most of the variance is in
the first few PCA components, and that the projection of the trajectory onto
any subspace spanned by PCA components is a Lissajous curve. We generalize
these results to a random walk with momentum and to an Ornstein-Uhlenbeck
processes (i.e., a random walk in a quadratic potential) and show that in high
dimensions the walk is not mean reverting, but will instead be trapped at a
fixed distance from the minimum. We finally compare the distribution of PCA
variances and the PCA projected training trajectories of a linear model trained
on CIFAR-10 and ResNet-50-v2 trained on Imagenet and find that the distribution
of PCA variances resembles a random walk with drift.
Hierarchical Graph Representation Learning with Differentiable Pooling
Authors: Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec
Date: 2018-06-22 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: General Classification, Graph Classification, Graph Neural Network, Graph Representation Learning, Link Prediction, Node Classification, Representation Learning

Recently, graph neural networks (GNNs) have revolutionized the field of graph
representation learning through effectively learned node embeddings, and
achieved state-of-the-art results in tasks such as node classification and link
prediction. However, current GNN methods are inherently flat and do not learn
hierarchical representations of graphs---a limitation that is especially
problematic for the task of graph classification, where the goal is to predict
the label associated with an entire graph. Here we propose DiffPool, a
differentiable graph pooling module that can generate hierarchical
representations of graphs and can be combined with various graph neural network
architectures in an end-to-end fashion. DiffPool learns a differentiable soft
cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a
set of clusters, which then form the coarsened input for the next GNN layer.
Our experimental results show that combining existing GNN methods with DiffPool
yields an average improvement of 5-10% accuracy on graph classification
benchmarks, compared to all existing pooling approaches, achieving a new
state-of-the-art on four out of five benchmark data sets.
Using NLP on news headlines to predict index trends
Authors: Marc Velay, Fabrice Daniel
Date: 2018-06-22 00:00:00

This paper attempts to provide a state of the art in trend prediction using
news headlines. We present the research done on predicting DJIA trends using
Natural Language Processing. We will explain the different algorithms we have
used as well as the various embedding techniques attempted. We rely on
statistical and deep learning models in order to extract information from the
corpuses.
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
Authors: Ting Chen, Martin Renqiang Min, Yizhou Sun
Date: 2018-06-21 00:00:00
Proceeding: ICML 2018 7

Conventional embedding methods directly associate each symbol with a
continuous embedding vector, which is equivalent to applying a linear
transformation based on a "one-hot" encoding of the discrete symbols. Despite
its simplicity, such approach yields the number of parameters that grows
linearly with the vocabulary size and can lead to overfitting. In this work, we
propose a much more compact K-way D-dimensional discrete encoding scheme to
replace the "one-hot" encoding. In the proposed "KD encoding", each symbol is
represented by a $D$-dimensional code with a cardinality of $K$, and the final
symbol embedding vector is generated by composing the code embedding vectors.
To end-to-end learn semantically meaningful codes, we derive a relaxed discrete
optimization approach based on stochastic gradient descent, which can be
generally applied to any differentiable computational graph with an embedding
layer. In our experiments with various applications from natural language
processing to graph convolutional networks, the total size of the embedding
layer can be reduced up to 98\% while achieving similar or better performance.
Target Robust Discriminant Analysis
Authors: Wouter M. Kouw, Marco Loog
Date: 2018-06-21 00:00:00

In practice, the data distribution at test time often differs, to a smaller or larger extent, from that of the original training data. Consequentially, the so-called source classifier, trained on the available labelled data, deteriorates on the test, or target, data. Domain adaptive classifiers aim to combat this problem, but typically assume some particular form of domain shift. Most are not robust to violations of domain shift assumptions and may even perform worse than their non-adaptive counterparts. We construct robust parameter estimators for discriminant analysis that guarantee performance improvements of the adaptive classifier over the non-adaptive source classifier.
Detection based Defense against Adversarial Examples from the Steganalysis Point of View
Authors: Jiayang Liu, Weiming Zhang, Yiwei Zhang, Dongdong Hou, Yujia Liu, Hongyue Zha, Nenghai Yu
Date: 2018-06-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: Steganalysis

Deep Neural Networks (DNNs) have recently led to significant improvements in
many fields. However, DNNs are vulnerable to adversarial examples which are
samples with imperceptible perturbations while dramatically misleading the
DNNs. Moreover, adversarial examples can be used to perform an attack on
various kinds of DNN based systems, even if the adversary has no access to the
underlying model. Many defense methods have been proposed, such as obfuscating
gradients of the networks or detecting adversarial examples. However it is
proved out that these defense methods are not effective or cannot resist
secondary adversarial attacks. In this paper, we point out that steganalysis
can be applied to adversarial examples detection, and propose a method to
enhance steganalysis features by estimating the probability of modifications
caused by adversarial attacks. Experimental results show that the proposed
method can accurately detect adversarial examples. Moreover, secondary
adversarial attacks cannot be directly performed to our method because our
method is not based on a neural network but based on high-dimensional
artificial features and FLD (Fisher Linear Discriminant) ensemble.
Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach
Authors: Arthur Colombini Gusmão, Alvaro Henrique Chaim Correia, Glauber De Bona, Fabio Gagliardi Cozman
Date: 2018-06-20 00:00:00
Tasks: Knowledge Base Completion

Knowledge bases are employed in a variety of applications from natural
language processing to semantic web search; alas, in practice their usefulness
is hurt by their incompleteness. Embedding models attain state-of-the-art
accuracy in knowledge base completion, but their predictions are notoriously
hard to interpret. In this paper, we adapt "pedagogical approaches" (from the
literature on neural networks) so as to interpret embedding models by
extracting weighted Horn rules from them. We show how pedagogical approaches
have to be adapted to take upon the large-scale relational aspects of knowledge
bases and show experimentally their strengths and weaknesses.
Optimal Seeding and Self-Reproduction from a Mathematical Point of View
Authors: Rita Gitik
Date: 2018-06-20 00:00:00

P. Kabamba developed generation theory as a tool for studying self-reproducing systems. We provide an alternative definition of a generation system and give a complete solution to the problem of finding optimal seeds for a finite self-replicating system. We also exhibit examples illustrating a connection between self-replication and fixed-point theory.
Convolutional Neural Networks to Enhance Coded Speech
Authors: Ziyue Zhao, Huijun Liu, Tim Fingscheidt
Date: 2018-06-25 00:00:00
Tasks: Quantization

Enhancing coded speech suffering from far-end acoustic background noise,
quantization noise, and potentially transmission errors, is a challenging task.
In this work we propose two postprocessing approaches applying convolutional
neural networks (CNNs) either in the time domain or the cepstral domain to
enhance the coded speech without any modification of the codecs. The time
domain approach follows an end-to-end fashion, while the cepstral domain
approach uses analysis-synthesis with cepstral domain features. The proposed
postprocessors in both domains are evaluated for various narrowband and
wideband speech codecs in a wide range of conditions. The proposed
postprocessor improves speech quality (PESQ) by up to 0.25 MOS-LQO points for
G.711, 0.30 points for G.726, 0.82 points for G.722, and 0.26 points for
adaptive multirate wideband codec (AMR-WB). In a subjective CCR listening test,
the proposed postprocessor on G.711-coded speech exceeds the speech quality of
an ITU-T-standardized postfilter by 0.36 CMOS points, and obtains a clear
preference of 1.77 CMOS points compared to G.711, even en par with uncoded
speech. The source code for the cepstral domain approach to enhance G.711-coded
speech is made available.
The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games
Authors: Zhengxing Chen, Truong-Huy D Nguyen, Yuyu Xu, Chris Amato, Seth Cooper, Yizhou Sun, Magy Seif El-Nasr
Date: 2018-06-26 00:00:00

Multiplayer Online Battle Arena (MOBA) games have received increasing
popularity recently. In a match of such games, players compete in two teams of
five, each controlling an in-game avatars, known as heroes, selected from a
roster of more than 100. The selection of heroes, also known as pick or draft,
takes place before the match starts and alternates between the two teams until
each player has selected one hero. Heroes are designed with different strengths
and weaknesses to promote team cooperation in a game. Intuitively, heroes in a
strong team should complement each other's strengths and suppressing those of
opponents. Hero drafting is therefore a challenging problem due to the complex
hero-to-hero relationships to consider. In this paper, we propose a novel hero
recommendation system that suggests heroes to add to an existing team while
maximizing the team's prospect for victory. To that end, we model the drafting
between two teams as a combinatorial game and use Monte Carlo Tree Search
(MCTS) for estimating the values of hero combinations. Our empirical evaluation
shows that hero teams drafted by our recommendation algorithm have
significantly higher win rate against teams constructed by other baseline and
state-of-the-art strategies.
Leveraging Disease Progression Learning for Medical Image Recognition
Authors: Qicheng Lao, Thomas Fevens, Boyu Wang
Date: 2018-06-26 00:00:00

Unlike natural images, medical images often have intrinsic characteristics
that can be leveraged for neural network learning. For example, images that
belong to different stages of a disease may continuously follow a certain
progression pattern. In this paper, we propose a novel method that leverages
disease progression learning for medical image recognition. In our method,
sequences of images ordered by disease stages are learned by a neural network
that consists of a shared vision model for feature extraction and a long
short-term memory network for the learning of stage sequences. Auxiliary vision
outputs are also included to capture stage features that tend to be discrete
along the disease progression. Our proposed method is evaluated on a public
diabetic retinopathy dataset, and achieves about 3.3% improvement in disease
staging accuracy, compared to the baseline method that does not use disease
progression learning.
Cardiopulmonary resuscitation quality parameters from motion capture data using Differential Evolution fitting of sinusoids
Authors: Christian Lins, Daniel Eckhoff, Andreas Klausen, Sandra Hellmers, Andreas Hein, Sebastian Fudickar
Date: 2018-06-26 00:00:00

Cardiopulmonary resuscitation (CPR) is alongside electrical defibrillation the most crucial countermeasure for sudden cardiac arrest, which affects thousands of individuals every year. In this paper, we present a novel approach including sinusoid models that use skeletal motion data from an RGB-D (Kinect) sensor and the Differential Evolution (DE) optimization algorithm to dynamically fit sinusoidal curves to derive frequency and depth parameters for cardiopulmonary resuscitation training. It is intended to be part of a robust and easy-to-use feedback system for CPR training, allowing its use for unsupervised training. The accuracy of this DE-based approach is evaluated in comparison with data of 28 participants recorded by a state-of-the-art training mannequin. We optimized the DE algorithm hyperparameters and showed that with these optimized parameters the frequency of the CPR is recognized with a median error of $\pm 2.9$ compressions per minute compared to the reference training mannequin.
Conditional Generators of Words Definitions
Authors: Artyom Gadetsky, Ilya Yakubovskiy, Dmitry Vetrov
Date: 2018-06-26 00:00:00
Proceeding: ACL 2018 7

We explore recently introduced definition modeling technique that provided
the tool for evaluation of different distributed vector representations of
words through modeling dictionary definitions of words. In this work, we study
the problem of word ambiguities in definition modeling and propose a possible
solution by employing latent variable modeling and soft attention mechanisms.
Our quantitative and qualitative evaluation and analysis of the model shows
that taking into account words ambiguity and polysemy leads to performance
improvement.
A Theory of Diagnostic Interpretation in Supervised Classification
Authors: Anirban Mukhopadhyay
Date: 2018-06-26 00:00:00
Tasks: Classification, Diagnostic, General Classification

Interpretable deep learning is a fundamental building block towards safer AI,
especially when the deployment possibilities of deep learning-based
computer-aided medical diagnostic systems are so eminent. However, without a
computational formulation of black-box interpretation, general interpretability
research rely heavily on subjective bias. Clear decision structure of the
medical diagnostics lets us approximate the decision process of a radiologist
as a model - removed from subjective bias. We define the process of
interpretation as a finite communication between a known model and a black-box
model to optimally map the black box's decision process in the known model.
Consequently, we define interpretability as maximal information gain over the
initial uncertainty about the black-box's decision within finite communication.
We relax this definition based on the observation that diagnostic
interpretation is typically achieved by a process of minimal querying. We
derive an algorithm to calculate diagnostic interpretability. The usual
question of accuracy-interpretability tradeoff, i.e. whether a black-box
model's prediction accuracy is dependent on its ability to be interpreted by a
known source model, does not arise in this theory. With multiple example
simulation experiments of various complexity levels, we demonstrate the working
of such a theoretical model in synthetic supervised classification scenarios.