pwc-archive/papers-with-abstracts huggingface dataset:
A frame semantic overview of NLP-based information extraction for cancer-related EHR notes
Authors: Surabhi Datta, Elmer V Bernstam, Kirk Roberts
Date: 2019-04-02 00:00:00
Tasks: Articles

Objective: There is a lot of information about cancer in Electronic Health
Record (EHR) notes that can be useful for biomedical research provided natural
language processing (NLP) methods are available to extract and structure this
information. In this paper, we present a scoping review of existing clinical
NLP literature for cancer. Methods: We identified studies describing an NLP
method to extract specific cancer-related information from EHR sources from
PubMed, Google Scholar, ACL Anthology, and existing reviews. Two exclusion
criteria were used in this study. We excluded articles where the extraction
techniques used were too broad to be represented as frames and also where very
low-level extraction methods were used. 79 articles were included in the final
review. We organized this information according to frame semantic principles to
help identify common areas of overlap and potential gaps. Results: Frames were
created from the reviewed articles pertaining to cancer information such as
cancer diagnosis, tumor description, cancer procedure, breast cancer diagnosis,
prostate cancer diagnosis and pain in prostate cancer patients. These frames
included both a definition as well as specific frame elements (i.e. extractable
attributes). We found that cancer diagnosis was the most common frame among the
reviewed papers (36 out of 79), with recent work focusing on extracting
information related to treatment and breast cancer diagnosis. Conclusion: The
list of common frames described in this paper identifies important
cancer-related information extracted by existing NLP techniques and serves as a
useful resource for future researchers requiring cancer information extracted
from EHR notes. We also argue, due to the heavy duplication of cancer NLP
systems, that a general purpose resource of annotated cancer frames and
corresponding NLP tools would be valuable.
Identifying disease-free chest X-ray images with deep transfer learning
Authors: Ken C. L. Wong, Mehdi Moradi, Joy Wu, Tanveer Syeda-Mahmood
Date: 2019-04-02 00:00:00
Tasks: Transfer Learning

Chest X-rays (CXRs) are among the most commonly used medical image
modalities. They are mostly used for screening, and an indication of disease
typically results in subsequent tests. As this is mostly a screening test used
to rule out chest abnormalities, the requesting clinicians are often interested
in whether a CXR is normal or not. A machine learning algorithm that can
accurately screen out even a small proportion of the "real normal" exams out of
all requested CXRs would be highly beneficial in reducing the workload for
radiologists. In this work, we report a deep neural network trained for
classifying CXRs with the goal of identifying a large number of normal
(disease-free) images without risking the discharge of sick patients. We use an
ImageNet-pretrained Inception-ResNet-v2 model to provide the image features,
which are further used to train a model on CXRs labelled by expert
radiologists. The probability threshold for classification is optimized for
100% precision for the normal class, ensuring no sick patients are released. At
this threshold we report an average recall of 50%. This means that the proposed
solution has the potential to cut in half the number of disease-free CXRs
examined by radiologists, without risking the discharge of sick patients.
Improving Robot Success Detection using Static Object Data
Authors: Rosario Scalise, Jesse Thomason, Yonatan Bisk, Siddhartha Srinivasa
Date: 2019-04-02 00:00:00
Tasks: Object

We use static object data to improve success detection for stacking objects on and nesting objects in one another. Such actions are necessary for certain robotics tasks, e.g., clearing a dining table or packing a warehouse bin. However, using an RGB-D camera to detect success can be insufficient: same-colored objects can be difficult to differentiate, and reflective silverware cause noisy depth camera perception. We show that adding static data about the objects themselves improves the performance of an end-to-end pipeline for classifying action outcomes. Images of the objects, and language expressions describing them, encode prior geometry, shape, and size information that refine classification accuracy. We collect over 13 hours of egocentric manipulation data for training a model to reason about whether a robot successfully placed unseen objects in or on one another. The model achieves up to a 57% absolute gain over the task baseline on pairs of previously unseen objects.
MVX-Net: Multimodal VoxelNet for 3D Object Detection
Authors: Vishwanath A. Sindagi, Yin Zhou, Oncel Tuzel
Date: 2019-04-02 00:00:00
Tasks: 3D Object Detection, Object, object-detection, Object Detection

Many recent works on 3D object detection have focused on designing neural
network architectures that can consume point cloud data. While these approaches
demonstrate encouraging performance, they are typically based on a single
modality and are unable to leverage information from other modalities, such as
a camera. Although a few approaches fuse data from different modalities, these
methods either use a complicated pipeline to process the modalities
sequentially, or perform late-fusion and are unable to learn interaction
between different modalities at early stages. In this work, we present
PointFusion and VoxelFusion: two simple yet effective early-fusion approaches
to combine the RGB and point cloud modalities, by leveraging the recently
introduced VoxelNet architecture. Evaluation on the KITTI dataset demonstrates
significant improvements in performance over approaches which only use point
cloud data. Furthermore, the proposed method provides results competitive with
the state-of-the-art multimodal algorithms, achieving top-2 ranking in five of
the six bird's eye view and 3D detection categories on the KITTI benchmark, by
using a simple single stage network.
Sequential Adaptive Design for Jump Regression Estimation
Authors: Chiwoo Park, Peihua Qiu, Jennifer Carpena-Núñez, Rahul Rao, Michael Susner, Benji Maruyama
Date: 2019-04-02 00:00:00
Tasks: Active Learning, regression

Selecting input variables or design points for statistical models has been of great interest in adaptive design and active learning. Motivated by two scientific examples, this paper presents a strategy of selecting the design points for a regression model when the underlying regression function is discontinuous. The first example we undertook was for the purpose of accelerating imaging speed in a high resolution material imaging; the second was use of sequential design for the purpose of mapping a chemical phase diagram. In both examples, the underlying regression functions have discontinuities, so many of the existing design optimization approaches cannot be applied because they mostly assume a continuous regression function. Although some existing adaptive design strategies developed from treed regression models can handle the discontinuities, the Bayesian approaches come with computationally expensive Markov Chain Monte Carlo techniques for posterior inferences and subsequent design point selections, which is not appropriate for the first motivating example that requires computation at least faster than the original imaging speed. In addition, the treed models are based on the domain partitioning that are inefficient when the discontinuities occurs over complex sub-domain boundaries. We propose a simple and effective adaptive design strategy for a regression analysis with discontinuities: some statistical properties with a fixed design will be presented first, and then these properties will be used to propose a new criterion of selecting the design points for the regression analysis. Sequential design with the new criterion will be presented with comprehensive simulated examples, and its application to the two motivating examples will be presented.
Sparse Bounded Degree Sum of Squares Optimization for Certifiably Globally Optimal Rotation Averaging
Authors: Matthew Giamou, Filip Maric, Valentin Peretroukhin, Jonathan Kelly
Date: 2019-04-02 00:00:00
Tasks: State Estimation

Estimating unknown rotations from noisy measurements is an important step in SfM and other 3D vision tasks. Typically, local optimization methods susceptible to returning suboptimal local minima are used to solve the rotation averaging problem. A new wave of approaches that leverage convex relaxations have provided the first formal guarantees of global optimality for state estimation techniques involving SO(3). However, most of these guarantees are only applicable when the measurement error introduced by noise is within a certain bound that depends on the problem instance's structure. In this paper, we cast rotation averaging as a polynomial optimization problem over unit quaternions to produce the first rotation averaging method that is formally guaranteed to provide a certifiably globally optimal solution for \textit{any} problem instance. This is achieved by formulating and solving a sparse convex sum of squares (SOS) relaxation of the problem. We provide an open source implementation of our algorithm and experiments, demonstrating the benefits of our globally optimal approach.
Generating Labels for Regression of Subjective Constructs using Triplet Embeddings
Authors: Karel Mundnich, Brandon M. Booth, Benjamin Girault, Shrikanth Narayanan
Date: 2019-04-02 00:00:00
Tasks: regression, Triplet

Human annotations serve an important role in computational models where the target constructs under study are hidden, such as dimensions of affect. This is especially relevant in machine learning, where subjective labels derived from related observable signals (e.g., audio, video, text) are needed to support model training and testing. Current research trends focus on correcting artifacts and biases introduced by annotators during the annotation process while fusing them into a single annotation. In this work, we propose a novel annotation approach using triplet embeddings. By lifting the absolute annotation process to relative annotations where the annotator compares individual target constructs in triplets, we leverage the accuracy of comparisons over absolute ratings by human annotators. We then build a 1-dimensional embedding in Euclidean space that is indexed in time and serves as a label for regression. In this setting, the annotation fusion occurs naturally as a union of sets of sampled triplet comparisons among different annotators. We show that by using our proposed sampling method to find an embedding, we are able to accurately represent synthetic hidden constructs in time under noisy sampling conditions. We further validate this approach using human annotations collected from Mechanical Turk and show that we can recover the underlying structure of the hidden construct up to bias and scaling factors.
A Strong Baseline for Domain Adaptation and Generalization in Medical Imaging
Authors: Li Yao, Jordan Prosky, Ben Covington, Kevin Lyman
Date: 2019-04-02 00:00:00
Tasks: Domain Adaptation, Multi-target Domain Adaptation

This work provides a strong baseline for the problem of multi-source
multi-target domain adaptation and generalization in medical imaging. Using a
diverse collection of ten chest X-ray datasets, we empirically demonstrate the
benefits of training medical imaging deep learning models on varied patient
populations for generalization to out-of-sample domains.
Towards annotation-efficient segmentation via image-to-image translation
Authors: Eugene Vorontsov, Pavlo Molchanov, Christopher Beckham, Jan Kautz, Samuel Kadoury
Date: 2019-04-02 00:00:00
Tasks: Brain Tumor Segmentation, Decoder, Image-to-Image Translation, Segmentation, Semantic Segmentation, Translation, Tumor Segmentation

Often in medical imaging, it is prohibitively challenging to produce enough boundary annotations to train deep neural networks for accurate tumor segmentation. We propose the use of weak labels about whether an image presents tumor or whether it is absent to extend training over images that lack these annotations. Specifically, we propose a semi-supervised framework that employs unpaired image-to-image translation between two domains, presence vs. absence of cancer, as the unsupervised objective. We conjecture that translation helps segmentation -- both require the target to be separated from the background. We encode images into two codes: one that is common to both domains and one that is unique to the presence domain. Decoding from the common code yields healthy images; decoding with the addition of the unique code produces a residual change to this image that adds cancer. Translation proceeds from presence to absence and vice versa. In the first case, the tumor is re-added to the image and we successfully exploit the residual decoder to also perform segmentation. In the second case, unique codes are sampled, producing a distribution of possible tumors. To validate the method, we created challenging synthetic tasks and tumor segmentation datasets from public BRATS (brain, MRI) and LitS (liver, CT) datasets. We show a clear improvement (0.83 Dice on brain, 0.74 on liver) over baseline semi-supervised training with autoencoding (0.73, 0.66) and a mean teacher approach (0.75, 0.69), demonstrating the ability to generalize from smaller distributions of annotated samples.
Identification, Interpretability, and Bayesian Word Embeddings
Authors: Adam M. Lauretig
Date: 2019-04-02 00:00:00
Tasks: regression, Word Embeddings

Social scientists have recently turned to analyzing text using tools from
natural language processing like word embeddings to measure concepts like
ideology, bias, and affinity. However, word embeddings are difficult to use in
the regression framework familiar to social scientists: embeddings are are
neither identified, nor directly interpretable. I offer two advances on
standard embedding models to remedy these problems. First, I develop Bayesian
Word Embeddings with Automatic Relevance Determination priors, relaxing the
assumption that all embedding dimensions have equal weight. Second, I apply
work identifying latent variable models to anchor the dimensions of the
resulting embeddings, identifying them, and making them interpretable and
usable in a regression. I then apply this model and anchoring approach to two
cases, the shift in internationalist rhetoric in the American presidents'
inaugural addresses, and the relationship between bellicosity in American
foreign policy decision-makers' deliberations. I find that inaugural addresses
became less internationalist after 1945, which goes against the conventional
wisdom, and that an increase in bellicosity is associated with an increase in
hostile actions by the United States, showing that elite deliberations are not
cheap talk, and helping confirm the validity of the model.
Lessons from Building Acoustic Models with a Million Hours of Speech
Authors: Sree Hari Krishnan Parthasarathi, Nikko Strom
Date: 2019-04-02 00:00:00
Tasks: Decoder

This is a report of our lessons learned building acoustic models from 1
Million hours of unlabeled speech, while labeled speech is restricted to 7,000
hours. We employ student/teacher training on unlabeled data, helping scale out
target generation in comparison to confidence model based methods, which
require a decoder and a confidence model. To optimize storage and to
parallelize target generation, we store high valued logits from the teacher
model. Introducing the notion of scheduled learning, we interleave learning on
unlabeled and labeled data. To scale distributed training across a large number
of GPUs, we use BMUF with 64 GPUs, while performing sequence training only on
labeled data with gradient threshold compression SGD using 16 GPUs. Our
experiments show that extremely large amounts of data are indeed useful; with
little hyper-parameter tuning, we obtain relative WER improvements in the 10 to
20% range, with higher gains in noisier conditions.
Towards Human Body-Part Learning for Model-Free Gait Recognition
Authors: Imad Rida
Date: 2019-04-02 00:00:00
Tasks: Gait Recognition

Gait based biometric aims to discriminate among people by the way or manner
they walk. It represents a biometric at distance which has many advantages over
other biometric modalities. State-of-the-art methods require a limited
cooperation from the individuals. Consequently, contrary to other modalities,
gait is a non-invasive approach. As a behavioral analysis, gait is difficult to
circumvent. Moreover, gait can be performed without the subject being aware of
it. Consequently, it is more difficult to try to tamper one own biometric
signature. In this paper we review different features and approaches used in
gait recognition. A novel method able to learn the discriminative human
body-parts to improve the recognition accuracy will be introduced. Extensive
experiments will be performed on CASIA gait benchmark database and results will
be compared to state-of-the-art methods.
Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts
Authors: Timo Schick, Hinrich Schütze
Date: 2019-04-02 00:00:00
Proceeding: NAACL 2019 6
Tasks: Form, Word Embeddings

Learning high-quality embeddings for rare words is a hard problem because of
sparse context information. Mimicking (Pinter et al., 2017) has been proposed
as a solution: given embeddings learned by a standard algorithm, a model is
first trained to reproduce embeddings of frequent words from their surface form
and then used to compute embeddings for rare words. In this paper, we introduce
attentive mimicking: the mimicking model is given access not only to a word's
surface form, but also to all available contexts and learns to attend to the
most informative and reliable contexts for computing an embedding. In an
evaluation on four tasks, we show that attentive mimicking outperforms previous
work for both rare and medium-frequency words. Thus, compared to previous work,
attentive mimicking improves embeddings for a much larger part of the
vocabulary, including the medium-frequency range.
Generative-Discriminative Complementary Learning
Authors: Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang, Kayhan Batmanghelich
Date: 2019-04-02 00:00:00

Majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increases. In this paper, we study the complementary learning problem. Unlike ordinary labels, complementary labels are easy to obtain because an annotator only needs to provide a yes/no answer to a randomly chosen candidate class for each instance. We propose a generative-discriminative complementary learning method that estimates the ordinary labels by modeling both the conditional (discriminative) and instance (generative) distributions. Our method, we call Complementary Conditional GAN (CCGAN), improves the accuracy of predicting ordinary labels and can generate high-quality instances in spite of weak supervision. In addition to the extensive empirical studies, we also theoretically show that our model can retrieve the true conditional distribution from the complementarily-labeled data.
Structural Scaffolds for Citation Intent Classification in Scientific Publications
Authors: Arman Cohan, Waleed Ammar, Madeleine van Zuylen, Field Cady
Date: 2019-04-02 00:00:00
Proceeding: NAACL 2019 6
Tasks: ARC, Citation Intent Classification, Classification, General Classification, intent-classification, Intent Classification, Reading Comprehension, Sentence Classification

Identifying the intent of a citation in scientific papers (e.g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature. We propose structural scaffolds, a multitask model to incorporate structural information of scientific papers into citations for effective classification of citation intents. Our model achieves a new state-of-the-art on an existing ACL anthology dataset (ACL-ARC) with a 13.3% absolute increase in F1 score, without relying on external linguistic resources or hand-engineered features as done in existing methods. In addition, we introduce a new dataset of citation intents (SciCite) which is more than five times larger and covers multiple scientific domains compared with existing datasets. Our code and data are available at: https://github.com/allenai/scicite.
Inferring Which Medical Treatments Work from Reports of Clinical Trials
Authors: Eric Lehman, Jay DeYoung, Regina Barzilay, Byron C. Wallace
Date: 2019-04-02 00:00:00
Proceeding: NAACL 2019 6
Tasks: Articles

How do we know if a particular medical treatment actually works? Ideally one
would consult all available evidence from relevant clinical trials.
Unfortunately, such results are primarily disseminated in natural language
scientific articles, imposing substantial burden on those trying to make sense
of them. In this paper, we present a new task and corpus for making this
unstructured evidence actionable. The task entails inferring reported findings
from a full-text article describing a randomized controlled trial (RCT) with
respect to a given intervention, comparator, and outcome of interest, e.g.,
inferring if an article provides evidence supporting the use of aspirin to
reduce risk of stroke, as compared to placebo.
  We present a new corpus for this task comprising 10,000+ prompts coupled with
full-text articles describing RCTs. Results using a suite of models --- ranging
from heuristic (rule-based) approaches to attentive neural architectures ---
demonstrate the difficulty of the task, which we believe largely owes to the
lengthy, technical input texts. To facilitate further work on this important,
challenging problem we make the corpus, documentation, a website and
leaderboard, and code for baselines and evaluation available at
http://evidence-inference.ebm-nlp.com/.
Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings
Authors: Dorottya Demszky, Nikhil Garg, Rob Voigt, James Zou, Matthew Gentzkow, Jesse Shapiro, Dan Jurafsky
Date: 2019-04-02 00:00:00
Proceeding: NAACL 2019 6
Tasks: Clustering

We provide an NLP framework to uncover four linguistic dimensions of
political polarization in social media: topic choice, framing, affect and
illocutionary force. We quantify these aspects with existing lexical methods,
and propose clustering of tweet embeddings as a means to identify salient
topics for analysis across events; human evaluations show that our approach
generates more cohesive topics than traditional LDA-based models. We apply our
methods to study 4.4M tweets on 21 mass shootings. We provide evidence that the
discussion of these events is highly polarized politically and that this
polarization is primarily driven by partisan differences in framing rather than
topic choice. We identify framing devices, such as grounding and the
contrasting use of the terms "terrorist" and "crazy", that contribute to
polarization. Results pertaining to topic choice, affect and illocutionary
force suggest that Republicans focus more on the shooter and event-specific
facts (news) while Democrats focus more on the victims and call for policy
changes. Our work contributes to a deeper understanding of the way group
divisions manifest in language and to computational methods for studying them.