pwc-archive/papers-with-abstracts huggingface dataset:
Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases
Authors: Marcos Martínez-Romero, Martin J. O'Connor, Attila L. Egyedi, Debra Willrett, Josef Hardi, John Graybeal, Mark A. Musen
Date: 2019-03-21 00:00:00

Metadata-the machine-readable descriptions of the data-are increasingly seen
as crucial for describing the vast array of biomedical datasets that are
currently being deposited in public repositories. While most public
repositories have firm requirements that metadata must accompany submitted
datasets, the quality of those metadata is generally very poor. A key problem
is that the typical metadata acquisition process is onerous and time consuming,
with little interactive guidance or assistance provided to users. Secondary
problems include the lack of validation and sparse use of standardized terms or
ontologies when authoring metadata. There is a pressing need for improvements
to the metadata acquisition process that will help users to enter metadata
quickly and accurately. In this paper we outline a recommendation system for
metadata that aims to address this challenge. Our approach uses association
rule mining to uncover hidden associations among metadata values and to
represent them in the form of association rules. These rules are then used to
present users with real-time recommendations when authoring metadata. The
novelties of our method are that it is able to combine analyses of metadata
from multiple repositories when generating recommendations and can enhance
those recommendations by aligning them with ontology terms. We implemented our
approach as a service integrated into the CEDAR Workbench metadata authoring
platform, and evaluated it using metadata from two public biomedical
repositories: US-based National Center for Biotechnology Information (NCBI)
BioSample and European Bioinformatics Institute (EBI) BioSamples. The results
show that our approach is able to use analyses of previous entered metadata
coupled with ontology-based mappings to present users with accurate
recommendations when authoring metadata.
A Computer-Aided System for Determining the Application Range of a Warfarin Clinical Dosing Algorithm Using Support Vector Machines with a Polynomial Kernel Function
Authors: Ashkan Sharabiani, Adam Bress, William Galanter, Rezvan Nazempour, Houshang Darabi
Date: 2019-03-21 00:00:00
Tasks: General Classification

Determining the optimal initial dose for warfarin is a critically important
task. Several factors have an impact on the therapeutic dose for individual
patients, such as patients' physical attributes (Age, Height, etc.), medication
profile, co-morbidities, and metabolic genotypes (CYP2C9 and VKORC1). These
wide range factors influencing therapeutic dose, create a complex environment
for clinicians to determine the optimal initial dose. Using a sample of 4,237
patients, we have proposed a companion classification model to one of the most
popular dosing algorithms (International Warfarin Pharmacogenetics Consortium
(IWPC) clinical model), which identifies the appropriate cohort of patients for
applying this model. The proposed model functions as a clinical decision
support system which assists clinicians in dosing. We have developed a
classification model using Support Vector Machines, with a polynomial kernel
function to determine if applying the dose prediction model is appropriate for
a given patient. The IWPC clinical model will only be used if the patient is
classified as "Safe for model". By using the proposed methodology, the dosing
mode's prediction accuracy increases by 15 percent in terms of Root Mean
Squared Error and 17 percent in terms of Mean Absolute Error in dose estimates
of patients classified as "Safe for model".
Deep Learning with Anatomical Priors: Imitating Enhanced Autoencoders in Latent Space for Improved Pelvic Bone Segmentation in MRI
Authors: Duc Duy Pham, Gurbandurdy Dovletov, Sebastian Warwas, Stefan Landgraeber, Marcus Jäger, Josef Pauli
Date: 2019-03-21 00:00:00
Tasks: Decoder, Segmentation, Semantic Segmentation

We propose a 2D Encoder-Decoder based deep learning architecture for semantic
segmentation, that incorporates anatomical priors by imitating the encoder
component of an autoencoder in latent space. The autoencoder is additionally
enhanced by means of hierarchical features, extracted by an U-Net module. Our
suggested architecture is trained in an end-to-end manner and is evaluated on
the example of pelvic bone segmentation in MRI. A comparison to the standard
U-Net architecture shows promising improvements.
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus
Authors: Yan Zhang, Michael M. Zavlanos
Date: 2019-03-21 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose a distributed off-policy actor critic method to
solve multi-agent reinforcement learning problems. Specifically, we assume that
all agents keep local estimates of the global optimal policy parameter and
update their local value function estimates independently. Then, we introduce
an additional consensus step to let all the agents asymptotically achieve
agreement on the global optimal policy function. The convergence analysis of
the proposed algorithm is provided and the effectiveness of the proposed
algorithm is validated using a distributed resource allocation example.
Compared to relevant distributed actor critic methods, here the agents do not
share information about their local tasks, but instead they coordinate to
estimate the global policy function.
CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification
Authors: Zheng Tang, Milind Naphade, Ming-Yu Liu, Xiaodong Yang, Stan Birchfield, Shuo Wang, Ratnesh Kumar, David Anastasiu, Jenq-Neng Hwang
Date: 2019-03-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: object-detection, Object Detection, Vehicle Re-Identification

Urban traffic optimization using traffic cameras as sensors is driving the
need to advance state-of-the-art multi-target multi-camera (MTMC) tracking.
This work introduces CityFlow, a city-scale traffic camera dataset consisting
of more than 3 hours of synchronized HD videos from 40 cameras across 10
intersections, with the longest distance between two simultaneous cameras being
2.5 km. To the best of our knowledge, CityFlow is the largest-scale dataset in
terms of spatial coverage and the number of cameras/videos in an urban
environment. The dataset contains more than 200K annotated bounding boxes
covering a wide range of scenes, viewing angles, vehicle models, and urban
traffic flow conditions. Camera geometry and calibration information are
provided to aid spatio-temporal analysis. In addition, a subset of the
benchmark is made available for the task of image-based vehicle
re-identification (ReID). We conducted an extensive experimental evaluation of
baselines/state-of-the-art approaches in MTMC tracking, multi-target
single-camera (MTSC) tracking, object detection, and image-based ReID on this
dataset, analyzing the impact of different network architectures, loss
functions, spatio-temporal models and their combinations on task effectiveness.
An evaluation server is launched with the release of our benchmark at the 2019
AI City Challenge (https://www.aicitychallenge.org/) that allows researchers to
compare the performance of their newest techniques. We expect this dataset to
catalyze research in this field, propel the state-of-the-art forward, and lead
to deployed traffic optimization(s) in the real world.
Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain
Authors: Soheil Khorram, Melvin G McInnis, Emily Mower Provost
Date: 2019-03-21 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis, Time Series Averaging

DTW calculates the similarity or alignment between two signals, subject to
temporal warping. However, its computational complexity grows exponentially
with the number of time-series. Although there have been algorithms developed
that are linear in the number of time-series, they are generally quadratic in
time-series length. The exception is generalized time warping (GTW), which has
linear computational cost. Yet, it can only identify simple time warping
functions. There is a need for a new fast, high-quality multisequence alignment
algorithm. We introduce trainable time warping (TTW), whose complexity is
linear in both the number and the length of time-series. TTW performs alignment
in the continuous-time domain using a sinc convolutional kernel and a
gradient-based optimization technique. We compare TTW and GTW on 85 UCR
datasets in time-series averaging and classification. TTW outperforms GTW on
67.1% of the datasets for the averaging tasks, and 61.2% of the datasets for
the classification tasks.
Low Resource Text Classification with ULMFit and Backtranslation
Authors: Sam Shleifer
Date: 2019-03-21 00:00:00
Tasks: Classification, Data Augmentation, General Classification, text-classification, Text Classification

In computer vision, virtually every state-of-the-art deep learning system is
trained with data augmentation. In text classification, however, data
augmentation is less widely practiced because it must be performed before
training and risks introducing label noise. We augment the IMDB movie reviews
dataset with examples generated by two families of techniques: random token
perturbations introduced by Wei and Zou [2019] and backtranslation --
translating to a second language then back to English. In low resource
environments, backtranslation generates significant improvement on top of the
state of-the-art ULMFit model. A ULMFit model pretrained on wikitext103 and
then fine-tuned on only 50 IMDB examples and 500 synthetic examples generated
by backtranslation achieves 80.6% accuracy, an 8.1% improvement over the
augmentation-free baseline with only 9 minutes of additional training time.
Random token perturbations do not yield any improvements but incur equivalent
computational cost. The benefits of training with backtranslated examples
decreases with the size of the available training data. On the full dataset,
neither augmentation technique improves upon ULMFit's state of the art
performance. We address this by using backtranslations as a form of test time
augmentation as well as ensembling ULMFit with other models, and achieve small
improvements.
Inferring Compact Representations for Efficient Natural Language Understanding of Robot Instructions
Authors: Siddharth Patki, Andrea F. Daniele, Matthew R. Walter, Thomas M. Howard
Date: 2019-03-21 00:00:00
Tasks: Natural Language Understanding

The speed and accuracy with which robots are able to interpret natural
language is fundamental to realizing effective human-robot interaction. A great
deal of attention has been paid to developing models and approximate inference
algorithms that improve the efficiency of language understanding. However,
existing methods still attempt to reason over a representation of the
environment that is flat and unnecessarily detailed, which limits scalability.
An open problem is then to develop methods capable of producing the most
compact environment model sufficient for accurate and efficient natural
language understanding. We propose a model that leverages environment-related
information encoded within instructions to identify the subset of observations
and perceptual classifiers necessary to perceive a succinct,
instruction-specific environment representation. The framework uses three
probabilistic graphical models trained from a corpus of annotated instructions
to infer salient scene semantics, perceptual classifiers, and grounded symbols.
Experimental results on two robots operating in different environments
demonstrate that by exploiting the content and the structure of the
instructions, our method learns compact environment representations that
significantly improve the efficiency of natural language symbol grounding.
Deep Radiomics for Brain Tumor Detection and Classification from Multi-Sequence MRI
Authors: Subhashis Banerjee, Sushmita Mitra, Francesco Masulli, Stefano Rovetta
Date: 2019-03-21 00:00:00
Tasks: General Classification, Transfer Learning

Glioma constitutes 80% of malignant primary brain tumors and is usually
classified as HGG and LGG. The LGG tumors are less aggressive, with slower
growth rate as compared to HGG, and are responsive to therapy. Tumor biopsy
being challenging for brain tumor patients, noninvasive imaging techniques like
Magnetic Resonance Imaging (MRI) have been extensively employed in diagnosing
brain tumors. Therefore automated systems for the detection and prediction of
the grade of tumors based on MRI data becomes necessary for assisting doctors
in the framework of augmented intelligence. In this paper, we thoroughly
investigate the power of Deep ConvNets for classification of brain tumors using
multi-sequence MR images. We propose novel ConvNet models, which are trained
from scratch, on MRI patches, slices, and multi-planar volumetric slices. The
suitability of transfer learning for the task is next studied by applying two
existing ConvNets models (VGGNet and ResNet) trained on ImageNet dataset,
through fine-tuning of the last few layers. LOPO testing, and testing on the
holdout dataset are used to evaluate the performance of the ConvNets. Results
demonstrate that the proposed ConvNets achieve better accuracy in all cases
where the model is trained on the multi-planar volumetric dataset. Unlike
conventional models, it obtains a testing accuracy of 95% for the low/high
grade glioma classification problem. A score of 97% is generated for
classification of LGG with/without 1p/19q codeletion, without any additional
effort towards extraction and selection of features. We study the properties of
self-learned kernels/ filters in different layers, through visualization of the
intermediate layer outputs. We also compare the results with that of
state-of-the-art methods, demonstrating a maximum improvement of 7% on the
grading performance of ConvNets and 9% on the prediction of 1p/19q codeletion
status.
Multi-Domain Adversarial Learning
Authors: Alice Schoenauer-Sebag, Louise Heinrich, Marc Schoenauer, Michele Sebag, Lani F. Wu, Steve J. Altschuler
Date: 2019-03-21 00:00:00
Proceeding: ICLR 2019 5
Tasks: Domain Adaptation

Multi-domain learning (MDL) aims at obtaining a model with minimal average
risk across multiple domains. Our empirical motivation is automated microscopy
data, where cultured cells are imaged after being exposed to known and unknown
chemical perturbations, and each dataset displays significant experimental
bias. This paper presents a multi-domain adversarial learning approach, MuLANN,
to leverage multiple datasets with overlapping but distinct class sets, in a
semi-supervised setting. Our contributions include: i) a bound on the average-
and worst-domain risk in MDL, obtained using the H-divergence; ii) a new loss
to accommodate semi-supervised multi-domain learning and domain adaptation;
iii) the experimental validation of the approach, improving on the state of the
art on two standard image benchmarks, and a novel bioimage dataset, Cell.
Scalable Similarity Joins of Tokenized Strings
Authors: Ahmed Metwally, Chun-Heng Huang
Date: 2019-03-21 00:00:00
Tasks: Data Integration, Fraud Detection

This work tackles the problem of fuzzy joining of strings that naturally
tokenize into meaningful substrings, e.g., full names. Tokenized-string joins
have several established applications in the context of data integration and
cleaning. This work is primarily motivated by fraud detection, where attackers
slightly modify tokenized strings, e.g., names on accounts, to create numerous
identities that she can use to defraud service providers, e.g., Google, and
LinkedIn. To detect such attacks, all the accounts are pair-wise compared, and
the resulting similar accounts are considered suspicious and are further
investigated. Comparing the tokenized-string features of a large number of
accounts requires an intuitive tokenized-string distance that can detect subtle
edits introduced by an adversary, and a very scalable algorithm. This is not
achievable by existing distance measure that are unintuitive, hard to tune, and
whose join algorithms are serial and hence unscalable. We define a novel
intuitive distance measure between tokenized strings, Normalized Setwise
Levenshtein Distance (NSLD). To the best of our knowledge, NSLD is the first
metric proposed for comparing tokenized strings. We propose a scalable
distributed framework, Tokenized-String Joiner (TSJ), that adopts existing
scalable string-join algorithms as building blocks to perform NSLD-joins. We
carefully engineer optimizations and approximations that dramatically improve
the efficiency of TSJ. The effectiveness of the TSJ framework is evident from
the evaluation conducted on tens of millions of tokenized-string names from
Google accounts. The superiority of the tokenized-string-specific TSJ framework
over the general-purpose metric-spaces joining algorithms has been established.
Convergence of Parameter Estimates for Regularized Mixed Linear Regression Models
Authors: Taiyao Wang, Ioannis Ch. Paschalidis
Date: 2019-03-21 00:00:00
Tasks: regression

We consider {\em Mixed Linear Regression (MLR)}, where training data have been generated from a mixture of distinct linear models (or clusters) and we seek to identify the corresponding coefficient vectors. We introduce a {\em Mixed Integer Programming (MIP)} formulation for MLR subject to regularization constraints on the coefficient vectors. We establish that as the number of training samples grows large, the MIP solution converges to the true coefficient vectors in the absence of noise. Subject to slightly stronger assumptions, we also establish that the MIP identifies the clusters from which the training samples were generated. In the special case where training data come from a single cluster, we establish that the corresponding MIP yields a solution that converges to the true coefficient vector even when training data are perturbed by (martingale difference) noise. We provide a counterexample indicating that in the presence of noise, the MIP may fail to produce the true coefficient vectors for more than one clusters. We also provide numerical results testing the MIP solutions in synthetic examples with noise.
SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding
Authors: Ilke Demir, Camilla Hahn, Kathryn Leonard, Geraldine Morin, Dana Rahbani, Athina Panotopoulou, Amelie Fondevilla, Elena Balashova, Bastien Durix, Adam Kortylewski
Date: 2019-03-21 00:00:00
Tasks: Deep Learning

We present SkelNetOn 2019 Challenge and Deep Learning for Geometric Shape Understanding workshop to utilize existing and develop novel deep learning architectures for shape understanding. We observed that unlike traditional segmentation and detection tasks, geometry understanding is still a new area for deep learning techniques. SkelNetOn aims to bring together researchers from different domains to foster learning methods on global shape understanding tasks. We aim to improve and evaluate the state-of-the-art shape understanding approaches, and to serve as reference benchmarks for future research. Similar to other challenges in computer vision, SkelNetOn proposes three datasets and corresponding evaluation methodologies; all coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2019 conference. In this paper, we describe and analyze characteristics of datasets, define the evaluation criteria of the public competitions, and provide baselines for each task.
Recovering the Lowest Layer of Deep Networks with High Threshold Activations
Authors: Surbhi Goel, Rina Panigrahy
Date: 2019-03-21 00:00:00
Proceeding: ICLR 2019 5
Tasks: BIG-bench Machine Learning, Learning Theory, Vocal Bursts Intensity Prediction

Giving provable guarantees for learning neural networks is a core challenge of machine learning theory. Most prior work gives parameter recovery guarantees for one hidden layer networks, however, the networks used in practice have multiple non-linear layers. In this work, we show how we can strengthen such results to deeper networks -- we address the problem of uncovering the lowest layer in a deep neural network under the assumption that the lowest layer uses a high threshold before applying the activation, the upper network can be modeled as a well-behaved polynomial and the input distribution is Gaussian.
Calibrated Top-1 Uncertainty estimates for classification by score based models
Authors: Adam M. Oberman, Chris Finlay, Alexander Iannantuono, Tiago Salvador
Date: 2019-03-21 00:00:00
Tasks: General Classification, Image Classification

While the accuracy of modern deep learning models has significantly improved in recent years, the ability of these models to generate uncertainty estimates has not progressed to the same degree. Uncertainty methods are designed to provide an estimate of class probabilities when predicting class assignment. While there are a number of proposed methods for estimating uncertainty, they all suffer from a lack of calibration: predicted probabilities can be off from empirical ones by a few percent or more. By restricting the scope of our predictions to only the probability of Top-1 error, we can decrease the calibration error of existing methods to less than one percent. As a result, the scores of the methods also improve significantly over benchmarks.
Multi-person Articulated Tracking with Spatial and Temporal Embeddings
Authors: Sheng Jin, Wentao Liu, Wanli Ouyang, Chen Qian
Date: 2019-03-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: Multi-Object Tracking, Multi-Person Pose Estimation, Multi-Person Pose Estimation and Tracking, Object Tracking, Pose Estimation, Pose Tracking

We propose a unified framework for multi-person pose estimation and tracking.
Our framework consists of two main components,~\ie~SpatialNet and TemporalNet.
The SpatialNet accomplishes body part detection and part-level data association
in a single frame, while the TemporalNet groups human instances in consecutive
frames into trajectories. Specifically, besides body part detection heatmaps,
SpatialNet also predicts the Keypoint Embedding (KE) and Spatial Instance
Embedding (SIE) for body part association. We model the grouping procedure into
a differentiable Pose-Guided Grouping (PGG) module to make the whole part
detection and grouping pipeline fully end-to-end trainable. TemporalNet extends
spatial grouping of keypoints to temporal grouping of human instances. Given
human proposals from two consecutive frames, TemporalNet exploits both
appearance features encoded in Human Embedding (HE) and temporally consistent
geometric features embodied in Temporal Instance Embedding (TIE) for robust
tracking. Extensive experiments demonstrate the effectiveness of our proposed
model. Remarkably, we demonstrate substantial improvements over the
state-of-the-art pose tracking method from 65.4\% to 71.8\% Multi-Object
Tracking Accuracy (MOTA) on the ICCV'17 PoseTrack Dataset.
Sparse2Dense: From direct sparse odometry to dense 3D reconstruction
Authors: Jiexiong Tang, John Folkesson, Patric Jensfelt
Date: 2019-03-21 00:00:00
Tasks: 3D Reconstruction, Depth Estimation, Depth Prediction, Monocular Visual Odometry, Visual Odometry, Visual Tracking

In this paper, we proposed a new deep learning based dense monocular SLAM
method. Compared to existing methods, the proposed framework constructs a dense
3D model via a sparse to dense mapping using learned surface normals. With
single view learned depth estimation as prior for monocular visual odometry, we
obtain both accurate positioning and high quality depth reconstruction. The
depth and normal are predicted by a single network trained in a tightly coupled
manner.Experimental results show that our method significantly improves the
performance of visual tracking and depth prediction in comparison to the
state-of-the-art in deep monocular dense SLAM.