pwc-archive/papers-with-abstracts huggingface dataset:
Biomedical term normalization of EHRs with UMLS
Authors: Naiara Perez, Montse Cuadros, German Rigau
Date: 2018-02-08 00:00:00
Proceeding: LREC 2018 5

This paper presents a novel prototype for biomedical term normalization of
electronic health record excerpts with the Unified Medical Language System
(UMLS) Metathesaurus. Despite being multilingual and cross-lingual by design,
we first focus on processing clinical text in Spanish because there is no
existing tool for this language and for this specific purpose. The tool is
based on Apache Lucene to index the Metathesaurus and generate mapping
candidates from input text. It uses the IXA pipeline for basic language
processing and resolves ambiguities with the UKB toolkit. It has been evaluated
by measuring its agreement with MetaMap in two English-Spanish parallel
corpora. In addition, we present a web-based interface for the tool.
Stable specification search in structural equation model with latent variables
Authors: Ridho Rahmadi, Perry Groot, Tom Heskes
Date: 2018-05-24 00:00:00
Tasks: Causal Discovery

In our previous study, we introduced stable specification search for
cross-sectional data (S3C). It is an exploratory causal method that combines
stability selection concept and multi-objective optimization to search for
stable and parsimonious causal structures across the entire range of model
complexities. In this study, we extended S3C to S3C-Latent, to model causal
relations between latent variables. We evaluated S3C-Latent on simulated data
and compared the results to those of PC-MIMBuild, an extension of the PC
algorithm, the state-of-the-art causal discovery method. The comparison showed
that S3C-Latent achieved better performance. We also applied S3C-Latent to
real-world data of children with attention deficit/hyperactivity disorder and
data about measuring mental abilities among pupils. The results are consistent
with those of previous studies.
SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes
Authors: Trung Pham, Thanh-Toan Do, Niko Sünderhauf, Ian Reid
Date: 2017-09-21 00:00:00
Tasks: Object, Semantic Segmentation

This paper presents SceneCut, a novel approach to jointly discover previously
unseen objects and non-object surfaces using a single RGB-D image. SceneCut's
joint reasoning over scene semantics and geometry allows a robot to detect and
segment object instances in complex scenes where modern deep learning-based
methods either fail to separate object instances, or fail to detect objects
that were not seen during training. SceneCut automatically decomposes a scene
into meaningful regions which either represent objects or scene surfaces. The
decomposition is qualified by an unified energy function over objectness and
geometric fitting. We show how this energy function can be optimized
efficiently by utilizing hierarchical segmentation trees. Moreover, we leverage
a pre-trained convolutional oriented boundary network to predict accurate
boundaries from images, which are used to construct high-quality region
hierarchies. We evaluate SceneCut on several different indoor environments, and
the results show that SceneCut significantly outperforms all the existing
methods.
Adversarially Learned One-Class Classifier for Novelty Detection
Authors: Mohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy, Ehsan Adeli
Date: 2018-02-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: Anomaly Detection, Novelty Detection, One-Class Classification, One-class classifier, Outlier Detection, Video Anomaly Detection

Novelty detection is the process of identifying the observation(s) that
differ in some respect from the training observations (the target class). In
reality, the novelty class is often absent during training, poorly sampled or
not well defined. Therefore, one-class classifiers can efficiently model such
problems. However, due to the unavailability of data from the novelty class,
training an end-to-end deep network is a cumbersome task. In this paper,
inspired by the success of generative adversarial networks for training deep
models in unsupervised and semi-supervised settings, we propose an end-to-end
architecture for one-class classification. Our architecture is composed of two
deep networks, each of which trained by competing with each other while
collaborating to understand the underlying concept in the target class, and
then classify the testing samples. One network works as the novelty detector,
while the other supports it by enhancing the inlier samples and distorting the
outliers. The intuition is that the separability of the enhanced inliers and
distorted outliers is much better than deciding on the original samples. The
proposed framework applies to different related applications of anomaly and
outlier detection in images and videos. The results on MNIST and Caltech-256
image datasets, along with the challenging UCSD Ped2 dataset for video anomaly
detection illustrate that our proposed method learns the target class
effectively and is superior to the baseline and state-of-the-art methods.
Imitating Latent Policies from Observation
Authors: Ashley D. Edwards, Himanshu Sahni, Yannick Schroecker, Charles L. Isbell
Date: 2018-05-21 00:00:00
Tasks: Imitation Learning

In this paper, we describe a novel approach to imitation learning that infers latent policies directly from state observations. We introduce a method that characterizes the causal effects of latent actions on observations while simultaneously predicting their likelihood. We then outline an action alignment procedure that leverages a small amount of environment interactions to determine a mapping between the latent and real-world actions. We show that this corrected labeling can be used for imitating the observed behavior, even though no expert actions are given. We evaluate our approach within classic control environments and a platform game and demonstrate that it performs better than standard approaches. Code for this work is available at https://github.com/ashedwards/ILPO.
A distinct approach to diagnose Dengue Fever with the help of Soft Set Theory
Authors: Maaz Amjad, fariha Bukhari, Iqra Ameer, Alexander Gelbukh
Date: 2018-05-22 00:00:00

Mathematics has played a substantial role to revolutionize the medical
science. Intelligent systems based on mathematical theories have proved to be
efficient in diagnosing various diseases. In this paper, we used an expert
system based on soft set theory and fuzzy set theory named as a soft expert
system to diagnose tropical disease dengue. The objective to use soft expert
system is to predict the risk level of a patient having dengue fever by using
input variables like age, TLC, SGOT, platelets count and blood pressure. The
proposed method explicitly demonstrates the exact percentage of the risk level
of dengue fever automatically circumventing for all possible (medical)
imprecisions.
AVID: Adversarial Visual Irregularity Detection
Authors: Mohammad Sabokrou, Masoud Pourreza, Mohsen Fayyaz, Rahim Entezari, Mahmood Fathy, Jürgen Gall, Ehsan Adeli
Date: 2018-05-24 00:00:00
Tasks: Anomaly Detection

Real-time detection of irregularities in visual data is very invaluable and
useful in many prospective applications including surveillance, patient
monitoring systems, etc. With the surge of deep learning methods in the recent
years, researchers have tried a wide spectrum of methods for different
applications. However, for the case of irregularity or anomaly detection in
videos, training an end-to-end model is still an open challenge, since often
irregularity is not well-defined and there are not enough irregular samples to
use during training. In this paper, inspired by the success of generative
adversarial networks (GANs) for training deep models in unsupervised or
self-supervised settings, we propose an end-to-end deep network for detection
and fine localization of irregularities in videos (and images). Our proposed
architecture is composed of two networks, which are trained in competing with
each other while collaborating to find the irregularity. One network works as a
pixel-level irregularity Inpainter, and the other works as a patch-level
Detector. After an adversarial self-supervised training, in which I tries to
fool D into accepting its inpainted output as regular (normal), the two
networks collaborate to detect and fine-segment the irregularity in any given
testing video. Our results on three different datasets show that our method can
outperform the state-of-the-art and fine-segment the irregularity.
Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing
Authors: Leye Wang, wenbin liu, Daqing Zhang, Yasha Wang, En Wang, Yongjian Yang
Date: 2018-04-19 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Sparse Mobile CrowdSensing (MCS) is a novel MCS paradigm where data inference
is incorporated into the MCS process for reducing sensing costs while its
quality is guaranteed. Since the sensed data from different cells (sub-areas)
of the target sensing area will probably lead to diverse levels of inference
data quality, cell selection (i.e., choose which cells of the target area to
collect sensed data from participants) is a critical issue that will impact the
total amount of data that requires to be collected (i.e., data collection
costs) for ensuring a certain level of quality. To address this issue, this
paper proposes a Deep Reinforcement learning based Cell selection mechanism for
Sparse MCS, called DR-Cell. First, we properly model the key concepts in
reinforcement learning including state, action, and reward, and then propose to
use a deep recurrent Q-network for learning the Q-function that can help decide
which cell is a better choice under a certain state during cell selection.
Furthermore, we leverage the transfer learning techniques to reduce the amount
of data required for training the Q-function if there are multiple correlated
MCS tasks that need to be conducted in the same target area. Experiments on
various real-life sensing datasets verify the effectiveness of DR-Cell over the
state-of-the-art cell selection mechanisms in Sparse MCS by reducing up to 15%
of sensed cells with the same data inference quality guarantee.
Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
Authors: Jizhe Wang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, Dik Lun Lee
Date: 2018-03-06 00:00:00
Proceeding: KDD 2018 5
Tasks: Graph Embedding, Recommendation Systems

Recommender systems (RSs) have been the most important technology for
increasing the business in Taobao, the largest online consumer-to-consumer
(C2C) platform in China. The billion-scale data in Taobao creates three major
challenges to Taobao's RS: scalability, sparsity and cold start. In this paper,
we present our technical solutions to address these three challenges. The
methods are based on the graph embedding framework. We first construct an item
graph from users' behavior history. Each item is then represented as a vector
using graph embedding. The item embeddings are employed to compute pairwise
similarities between all items, which are then used in the recommendation
process. To alleviate the sparsity and cold start problems, side information is
incorporated into the embedding framework. We propose two aggregation methods
to integrate the embeddings of items and the corresponding side information.
Experimental results from offline experiments show that methods incorporating
side information are superior to those that do not. Further, we describe the
platform upon which the embedding methods are deployed and the workflow to
process the billion-scale data in Taobao. Using online A/B test, we show that
the online Click-Through-Rate (CTRs) are improved comparing to the previous
recommendation methods widely used in Taobao, further demonstrating the
effectiveness and feasibility of our proposed methods in Taobao's live
production environment.
Incorporating Privileged Information to Unsupervised Anomaly Detection
Authors: Shubhranshu Shekhar, Leman Akoglu
Date: 2018-05-06 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

We introduce a new unsupervised anomaly detection ensemble called SPI which
can harness privileged information - data available only for training examples
but not for (future) test examples. Our ideas build on the Learning Using
Privileged Information (LUPI) paradigm pioneered by Vapnik et al. [19,17],
which we extend to unsupervised learning and in particular to anomaly
detection. SPI (for Spotting anomalies with Privileged Information) constructs
a number of frames/fragments of knowledge (i.e., density estimates) in the
privileged space and transfers them to the anomaly scoring space through
"imitation" functions that use only the partial information available for test
examples. Our generalization of the LUPI paradigm to unsupervised anomaly
detection shepherds the field in several key directions, including (i) domain
knowledge-augmented detection using expert annotations as PI, (ii) fast
detection using computationally-demanding data as PI, and (iii) early detection
using "historical future" data as PI. Through extensive experiments on
simulated and real datasets, we show that augmenting privileged information to
anomaly detection significantly improves detection performance. We also
demonstrate the promise of SPI under all three settings (i-iii); with PI
capturing expert knowledge, computationally expensive features, and future data
on three real world detection tasks.
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification
Authors: Max Simchowitz, Horia Mania, Stephen Tu, Michael. I. Jordan, Benjamin Recht
Date: 2018-02-22 00:00:00
Tasks: Time Series, Time Series Analysis

We prove that the ordinary least-squares (OLS) estimator attains nearly
minimax optimal performance for the identification of linear dynamical systems
from a single observed trajectory. Our upper bound relies on a generalization
of Mendelson's small-ball method to dependent data, eschewing the use of
standard mixing-time arguments. Our lower bounds reveal that these upper bounds
match up to logarithmic factors. In particular, we capture the correct
signal-to-noise behavior of the problem, showing that more unstable linear
systems are easier to estimate. This behavior is qualitatively different from
arguments which rely on mixing-time calculations that suggest that unstable
systems are more difficult to estimate. We generalize our technique to provide
bounds for a more general class of linear response time-series.
Learning from Noisy Web Data with Category-level Supervision
Authors: Li Niu, Qingtao Tang, Ashok Veeraraghavan, Ashu Sabharwal
Date: 2018-03-10 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification

As tons of photos are being uploaded to public websites (e.g., Flickr, Bing,
and Google) every day, learning from web data has become an increasingly
popular research direction because of freely available web resources, which is
also referred to as webly supervised learning. Nevertheless, the performance
gap between webly supervised learning and traditional supervised learning is
still very large, owning to the label noise of web data. To be exact, the
labels of images crawled from public websites are very noisy and often
inaccurate. Some existing works tend to facilitate learning from web data with
the aid of extra information, such as augmenting or purifying web data by
virtue of instance-level supervision, which is usually in demand of heavy
manual annotation. Instead, we propose to tackle the label noise by leveraging
more accessible category-level supervision. In particular, we build our method
upon variational autoencoder (VAE), in which the classification network is
attached on the hidden layer of VAE in a way that the classification network
and VAE can jointly leverage the category-level hybrid semantic information.
The effectiveness of our proposed method is clearly demonstrated by extensive
experiments on three benchmark datasets.
You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery
Authors: Adam Van Etten
Date: 2018-05-24 00:00:00
Tasks: object-detection, Object Detection

Detection of small objects in large swaths of imagery is one of the primary
problems in satellite imagery analytics. While object detection in ground-based
imagery has benefited from research into new deep learning approaches,
transitioning such technology to overhead imagery is nontrivial. Among the
challenges is the sheer number of pixels and geographic extent per image: a
single DigitalGlobe satellite image encompasses >64 km2 and over 250 million
pixels. Another challenge is that objects of interest are minuscule (often only
~10 pixels in extent), which complicates traditional computer vision
techniques. To address these issues, we propose a pipeline (You Only Look
Twice, or YOLT) that evaluates satellite images of arbitrary size at a rate of
>0.5 km2/s. The proposed approach can rapidly detect objects of vastly
different scales with relatively little training data over multiple sensors. We
evaluate large test images at native resolution, and yield scores of F1 > 0.8
for vehicle localization. We further explore resolution and object size
requirements by systematically testing the pipeline at decreasing resolution,
and conclude that objects only ~5 pixels in size can still be localized with
high confidence. Code is available at https://github.com/CosmiQ/yolt.
Estimating Carotid Pulse and Breathing Rate from Near-infrared Video of the Neck
Authors: Weixuan Chen, Javier Hernandez, Rosalind W. Picard
Date: 2018-05-24 00:00:00
Tasks: Template Matching

Objective: Non-contact physiological measurement is a growing research area
that allows capturing vital signs such as heart rate (HR) and breathing rate
(BR) comfortably and unobtrusively with remote devices. However, most of the
approaches work only in bright environments in which subtle
photoplethysmographic and ballistocardiographic signals can be easily analyzed
and/or require expensive and custom hardware to perform the measurements.
  Approach: This work introduces a low-cost method to measure subtle motions
associated with the carotid pulse and breathing movement from the neck using
near-infrared (NIR) video imaging. A skin reflection model of the neck was
established to provide a theoretical foundation for the method. In particular,
the method relies on template matching for neck detection, Principal Component
Analysis for feature extraction, and Hidden Markov Models for data smoothing.
  Main Results: We compared the estimated HR and BR measures with ones provided
by an FDA-cleared device in a 12-participant laboratory study: the estimates
achieved a mean absolute error of 0.36 beats per minute and 0.24 breaths per
minute under both bright and dark lighting.
  Significance: This work advances the possibilities of non-contact
physiological measurement in real-life conditions in which environmental
illumination is limited and in which the face of the person is not readily
available or needs to be protected. Due to the increasing availability of NIR
imaging devices, the described methods are readily scalable.
Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering
Authors: Israel Almodóvar-Rivera, Ranjan Maitra
Date: 2018-05-24 00:00:00
Tasks: Clustering

Commonly-used clustering algorithms usually find ellipsoidal, spherical or other regular-structured clusters, but are more challenged when the underlying groups lack formal structure or definition. Syncytial clustering is the name that we introduce for methods that merge groups obtained from standard clustering algorithms in order to reveal complex group structure in the data. Here, we develop a distribution-free fully-automated syncytial clustering algorithm that can be used with $k$-means and other algorithms. Our approach estimates the cumulative distribution function of the normed residuals from an appropriately fit $k$-groups model and calculates the estimated nonparametric overlap between each pair of clusters. Groups with high pairwise overlap are merged as long as the estimated generalized overlap decreases. Our methodology is always a top performer in identifying groups with regular and irregular structures in several datasets and can be applied to datasets with scatter or incomplete records. The approach is also used to identify the distinct kinds of gamma ray bursts in the Burst and Transient Source Experiment 4Br catalog and the distinct kinds of activation in a functional Magnetic Resonance Imaging study.
AutoAugment: Learning Augmentation Policies from Data
Authors: Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le
Date: 2018-05-24 00:00:00
Tasks: Data Augmentation, Domain Generalization, Fine-Grained Image Classification, Image Augmentation, Image Classification

Data augmentation is an effective technique for improving the accuracy of
modern image classifiers. However, current data augmentation implementations
are manually designed. In this paper, we describe a simple procedure called
AutoAugment to automatically search for improved data augmentation policies. In
our implementation, we have designed a search space where a policy consists of
many sub-policies, one of which is randomly chosen for each image in each
mini-batch. A sub-policy consists of two operations, each operation being an
image processing function such as translation, rotation, or shearing, and the
probabilities and magnitudes with which the functions are applied. We use a
search algorithm to find the best policy such that the neural network yields
the highest validation accuracy on a target dataset. Our method achieves
state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without
additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is
0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error
rate of 1.5%, which is 0.6% better than the previous state-of-the-art.
Augmentation policies we find are transferable between datasets. The policy
learned on ImageNet transfers well to achieve significant improvements on other
datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft,
and Stanford Cars.
Cross Domain Image Generation through Latent Space Exploration with Adversarial Loss
Authors: Yingjing Lu
Date: 2018-05-24 00:00:00
Tasks: Image Generation

Conditional domain generation is a good way to interactively control sample
generation process of deep generative models. However, once a conditional
generative model has been created, it is often expensive to allow it to adapt
to new conditional controls, especially the network structure is relatively
deep. We propose a conditioned latent domain transfer framework across latent
spaces of unconditional variational autoencoders(VAE). With this framework, we
can allow unconditionally trained VAEs to generate images in its domain with
conditionals provided by a latent representation of another domain. This
framework does not assume commonalities between two domains. We demonstrate
effectiveness and robustness of our model under widely used image datasets.