pwc-archive/papers-with-abstracts huggingface dataset:
StegaStamp: Invisible Hyperlinks in Physical Photographs
Authors: Matthew Tancik, Ben Mildenhall, Ren Ng
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2020 6
Tasks: Steganographics

Printed and digitally displayed photos have the ability to hide imperceptible digital data that can be accessed through internet-connected imaging systems. Another way to think about this is physical photographs that have unique QR codes invisibly embedded within them. This paper presents an architecture, algorithms, and a prototype implementation addressing this vision. Our key technical contribution is StegaStamp, a learned steganographic algorithm to enable robust encoding and decoding of arbitrary hyperlink bitstrings into photos in a manner that approaches perceptual invisibility. StegaStamp comprises a deep neural network that learns an encoding/decoding algorithm robust to image perturbations approximating the space of distortions resulting from real printing and photography. We demonstrates real-time decoding of hyperlinks in photos from in-the-wild videos that contain variation in lighting, shadows, perspective, occlusion and viewing distance. Our prototype system robustly retrieves 56 bit hyperlinks after error correction - sufficient to embed a unique code within every photo on the internet.
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
Authors: Kexin Huang, Jaan Altosaar, Rajesh Ranganath
Date: 2019-04-10 00:00:00
Tasks: Readmission Prediction

Clinical notes contain information about patients that goes beyond structured data like lab values and medications. However, clinical notes have been underused relative to structured data, because notes are high-dimensional and sparse. This work develops and evaluates representations of clinical notes using bidirectional transformers (ClinicalBERT). ClinicalBERT uncovers high-quality relationships between medical concepts as judged by humans. ClinicalBert outperforms baselines on 30-day hospital readmission prediction using both discharge summaries and the first few days of notes in the intensive care unit. Code and model parameters are available.
New Computational and Statistical Aspects of Regularized Regression with Application to Rare Feature Selection and Aggregation
Authors: Amin Jalali, Adel Javanmard, Maryam Fazel
Date: 2019-04-10 00:00:00
Tasks: feature selection

Prior knowledge on properties of a target model often come as discrete or
combinatorial descriptions. This work provides a unified computational
framework for defining norms that promote such structures. More specifically,
we develop associated tools for optimization involving such norms given only
the orthogonal projection oracle onto the non-convex set of desired models. As
an example, we study a norm, which we term the doubly-sparse norm, for
promoting vectors with few nonzero entries taking only a few distinct values.
We further discuss how the K-means algorithm can serve as the underlying
projection oracle in this case and how it can be efficiently represented as a
quadratically constrained quadratic program. Our motivation for the study of
this norm is regularized regression in the presence of rare features which
poses a challenge to various methods within high-dimensional statistics, and in
machine learning in general. The proposed estimation procedure is designed to
perform automatic feature selection and aggregation for which we develop
statistical bounds. The bounds are general and offer a statistical framework
for norm-based regularization. The bounds rely on novel geometric quantities on
which we attempt to elaborate as well.
Deep Neural Networks Ensemble for Detecting Medication Mentions in Tweets
Authors: Davy Weissenbacher, Abeed Sarker, Ari Klein, Karen O'Connor, Arjun Magge Ranganatha, Graciela Gonzalez-Hernandez
Date: 2019-04-10 00:00:00
Tasks: Ensemble Learning

Objective: After years of research, Twitter posts are now recognized as an important source of patient-generated data, providing unique insights into population health. A fundamental step to incorporating Twitter data in pharmacoepidemiological research is to automatically recognize medication mentions in tweets. Given that lexical searches for medication names may fail due to misspellings or ambiguity with common words, we propose a more advanced method to recognize them. Methods: We present Kusuri, an Ensemble Learning classifier, able to identify tweets mentioning drug products and dietary supplements. Kusuri ("medication" in Japanese) is composed of two modules. First, four different classifiers (lexicon-based, spelling-variant-based, pattern-based and one based on a weakly-trained neural network) are applied in parallel to discover tweets potentially containing medication names. Second, an ensemble of deep neural networks encoding morphological, semantical and long-range dependencies of important words in the tweets discovered is used to make the final decision. Results: On a balanced (50-50) corpus of 15,005 tweets, Kusuri demonstrated performances close to human annotators with 93.7% F1-score, the best score achieved thus far on this corpus. On a corpus made of all tweets posted by 113 Twitter users (98,959 tweets, with only 0.26% mentioning medications), Kusuri obtained 76.3% F1-score. There is not a prior drug extraction system that compares running on such an extremely unbalanced dataset. Conclusion: The system identifies tweets mentioning drug names with performance high enough to ensure its usefulness and ready to be integrated in larger natural language processing systems.
Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery
Authors: Yona Falinie A. Gaus, Neelanjan Bhowmik, Samet Akçay, Paolo M. Guillen-Garcia, Jack W. Barker, Toby P. Breckon
Date: 2019-04-10 00:00:00
Tasks: Anomaly Detection, General Classification, Object, object-detection, Object Detection

X-ray baggage security screening is widely used to maintain aviation and
transport security. Of particular interest is the focus on automated security
X-ray analysis for particular classes of object such as electronics, electrical
items, and liquids. However, manual inspection of such items is challenging
when dealing with potentially anomalous items. Here we present a dual
convolutional neural network (CNN) architecture for automatic anomaly detection
within complex security X-ray imagery. We leverage recent advances in
region-based (R-CNN), mask-based CNN (Mask R-CNN) and detection architectures
such as RetinaNet to provide object localisation variants for specific object
classes of interest. Subsequently, leveraging a range of established CNN object
and fine-grained category classification approaches we formulate within object
anomaly detection as a two-class problem (anomalous or benign). While the best
performing object localisation method is able to perform with 97.9% mean
average precision (mAP) over a six-class X-ray object detection problem,
subsequent two-class anomaly/benign classification is able to achieve 66%
performance for within object anomaly detection. Overall, this performance
illustrates both the challenge and promise of object-wise anomaly detection
within the context of cluttered X-ray security imagery.
CNM: An Interpretable Complex-valued Network for Matching
Authors: Qiuchi Li, Benyou Wang, Massimo Melucci
Date: 2019-04-10 00:00:00
Proceeding: NAACL 2019 6
Tasks: Benchmarking, Question Answering

This paper seeks to model human language by the mathematical framework of
quantum physics. With the well-designed mathematical formulations in quantum
physics, this framework unifies different linguistic units in a single
complex-valued vector space, e.g. words as particles in quantum states and
sentences as mixed systems. A complex-valued network is built to implement this
framework for semantic matching. With well-constrained complex-valued
components, the network admits interpretations to explicit physical meanings.
The proposed complex-valued network for matching (CNM) achieves comparable
performances to strong CNN and RNN baselines on two benchmarking question
answering (QA) datasets.
Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation
Authors: Junhwa Hur, Stefan Roth
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Occlusion Estimation, Optical Flow Estimation

Deep learning approaches to optical flow estimation have seen rapid progress
over the recent years. One common trait of many networks is that they refine an
initial flow estimate either through multiple stages or across the levels of a
coarse-to-fine representation. While leading to more accurate results, the
downside of this is an increased number of parameters. Taking inspiration from
both classical energy minimization approaches as well as residual networks, we
propose an iterative residual refinement (IRR) scheme based on weight sharing
that can be combined with several backbone networks. It reduces the number of
parameters, improves the accuracy, or even achieves both. Moreover, we show
that integrating occlusion prediction and bi-directional flow estimation into
our IRR scheme can further boost the accuracy. Our full network achieves
state-of-the-art results for both optical flow and occlusion estimation across
several standard datasets.
Advances in Natural Language Question Answering: A Review
Authors: K. S. D. Ishwari, A. K. R. R. Aneeze, S. Sudheesan, H. J. D. A. Karunaratne, A. Nugaliyadde, Y. Mallawarrachchi
Date: 2019-04-10 00:00:00
Tasks: Deep Learning, Question Answering

Question Answering has recently received high attention from artificial
intelligence communities due to the advancements in learning technologies.
Early question answering models used rule-based approaches and moved to the
statistical approach to address the vastly available information. However,
statistical approaches are shown to underperform in handling the dynamic nature
and the variation of language. Therefore, learning models have shown the
capability of handling the dynamic nature and variations in language. Many deep
learning methods have been introduced to question answering. Most of the deep
learning approaches have shown to achieve higher results compared to machine
learning and statistical methods. The dynamic nature of language has profited
from the nonlinear learning in deep learning. This has created prominent
success and a spike in work on question answering. This paper discusses the
successes and challenges in question answering question answering systems and
techniques that are used in these challenges.
Active Learning for Decision-Making from Imbalanced Observational Data
Authors: Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski
Date: 2019-04-10 00:00:00
Tasks: Active Learning, Decision Making

Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based decision-making in a task of deciding which action $a$ to take for a target unit after observing its covariates $\tilde{x}$ and predicted outcomes $\hat{p}(\tilde{y} \mid \tilde{x}, a)$. An example case is personalized medicine and the decision of which treatment to give to a patient. A common problem when learning these models from observational data is imbalance, that is, difference in treated/control covariate distributions, which is known to increase the upper bound of the expected ITE estimation error. We propose to assess the decision-making reliability by estimating the ITE model's Type S error rate, which is the probability of the model inferring the sign of the treatment effect wrong. Furthermore, we use the estimated reliability as a criterion for active learning, in order to collect new (possibly expensive) observations, instead of making a forced choice based on unreliable predictions. We demonstrate the effectiveness of this decision-making aware active learning in two decision-making tasks: in simulated data with binary outcomes and in a medical dataset with synthetic and continuous treatment outcomes.
Deep Learning Inversion of Electrical Resistivity Data
Authors: Bin Liu, Qian Guo, Shucai Li, Benchao Liu, Yuxiao Ren, Yonghao Pang, Xu Guo, Lanbo Liu, Peng Jiang
Date: 2019-04-10 00:00:00
Tasks: Deep Learning, Model Selection

The inverse problem of electrical resistivity surveys (ERSs) is difficult because of its nonlinear and ill-posed nature. For this task, traditional linear inversion methods still face challenges such as suboptimal approximation and initial model selection. Inspired by the remarkable nonlinear mapping ability of deep learning approaches, in this article, we propose to build the mapping from apparent resistivity data (input) to resistivity model (output) directly by convolutional neural networks (CNNs). However, the vertically varying characteristic of patterns in the apparent resistivity data may cause ambiguity when using CNNs with the weight sharing and effective receptive field properties. To address the potential issue, we supply an additional tier feature map to CNNs to help those aware of the relationship between input and output. Based on the prevalent U-Net architecture, we design our network (ERSInvNet) that can be trained end-to-end and can reach a very fast inference speed during testing. We further introduce a depth weighting function and a smooth constraint into loss function to improve inversion accuracy for the deep region and suppress false anomalies. Six groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed methods. According to the comprehensive qualitative analysis and quantitative comparison, ERSInvNet with tier feature map, smooth constraints, and depth weighting function together achieve the best performance.
Egocentric Visitors Localization in Cultural Sites
Authors: Francesco Ragusa, Antonino Furnari, Sebastiano Battiato, Giovanni Signorello, Giovanni Maria Farinella
Date: 2019-04-10 00:00:00

We consider the problem of localizing visitors in a cultural site from
egocentric (first person) images. Localization information can be useful both
to assist the user during his visit (e.g., by suggesting where to go and what
to see next) and to provide behavioral information to the manager of the
cultural site (e.g., how much time has been spent by visitors at a given
location? What has been liked most?). To tackle the problem, we collected a
large dataset of egocentric videos using two cameras: a head-mounted HoloLens
device and a chest-mounted GoPro. Each frame has been labeled according to the
location of the visitor and to what he was looking at. The dataset is freely
available in order to encourage research in this domain. The dataset is
complemented with baseline experiments performed considering a state-of-the-art
method for location-based temporal segmentation of egocentric videos.
Experiments show that compelling results can be achieved to extract useful
information for both the visitor and the site-manager.
Analysis of the Gradient Descent Algorithm for a Deep Neural Network Model with Skip-connections
Authors: Weinan E, Chao Ma, Qingcan Wang, Lei Wu
Date: 2019-04-10 00:00:00

The behavior of the gradient descent (GD) algorithm is analyzed for a deep
neural network model with skip-connections. It is proved that in the
over-parametrized regime, for a suitable initialization, with high probability
GD can find a global minimum exponentially fast. Generalization error estimates
along the GD path are also established. As a consequence, it is shown that when
the target function is in the reproducing kernel Hilbert space (RKHS) with a
kernel defined by the initialization, there exist generalizable early-stopping
solutions along the GD path. In addition, it is also shown that the GD path is
uniformly close to the functions given by the related random feature model.
Consequently, in this "implicit regularization" setting, the deep neural
network model deteriorates to a random feature model. Our results hold for
neural networks of any width larger than the input dimension.
Instance Segmentation of Biological Images Using Harmonic Embeddings
Authors: Victor Kulikov, Victor Lempitsky
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2020 6
Tasks: Clustering, Computational Efficiency, Instance Segmentation, Object, Segmentation, Semantic Segmentation

We present a new instance segmentation approach tailored to biological images, where instances may correspond to individual cells, organisms or plant parts. Unlike instance segmentation for user photographs or road scenes, in biological data object instances may be particularly densely packed, the appearance variation may be particularly low, the processing power may be restricted, while, on the other hand, the variability of sizes of individual instances may be limited. The proposed approach successfully addresses these peculiarities. Our approach describes each object instance using an expectation of a limited number of sine waves with frequencies and phases adjusted to particular object sizes and densities. At train time, a fully-convolutional network is learned to predict the object embeddings at each pixel using a simple pixelwise regression loss, while at test time the instances are recovered using clustering in the embedding space. In the experiments, we show that our approach outperforms previous embedding-based instance segmentation approaches on a number of biological datasets, achieving state-of-the-art on a popular CVPPP benchmark. This excellent performance is combined with computational efficiency that is needed for deployment to domain specialists. The source code of the approach is available at https://github.com/kulikovv/harmonic
Simple BERT Models for Relation Extraction and Semantic Role Labeling
Authors: Peng Shi, Jimmy Lin
Date: 2019-04-10 00:00:00
Tasks: Relation, Relation Extraction, Semantic Role Labeling

We present simple BERT-based models for relation extraction and semantic role
labeling. In recent years, state-of-the-art performance has been achieved using
neural models by incorporating lexical and syntactic features such as
part-of-speech tags and dependency trees. In this paper, extensive experiments
on datasets for these two tasks show that without using any external features,
a simple BERT-based model can achieve state-of-the-art performance. To our
knowledge, we are the first to successfully apply BERT in this manner. Our
models provide strong baselines for future research.
Attraction-Repulsion clustering with applications to fairness
Authors: Eustasio del Barrio, Hristo Inouzhe, Jean-Michel Loubes
Date: 2019-04-10 00:00:00
Tasks: Clustering, Diversity, Fairness

We consider the problem of diversity enhancing clustering, i.e, developing clustering methods which produce clusters that favour diversity with respect to a set of protected attributes such as race, sex, age, etc. In the context of fair clustering, diversity plays a major role when fairness is understood as demographic parity. To promote diversity, we introduce perturbations to the distance in the unprotected attributes that account for protected attributes in a way that resembles attraction-repulsion of charged particles in Physics. These perturbations are defined through dissimilarities with a tractable interpretation. Cluster analysis based on attraction-repulsion dissimilarities penalizes homogeneity of the clusters with respect to the protected attributes and leads to an improvement in diversity. An advantage of our approach, which falls into a pre-processing set-up, is its compatibility with a wide variety of clustering methods and whit non-Euclidean data. We illustrate the use of our procedures with both synthetic and real data and provide discussion about the relation between diversity, fairness, and cluster structure. Our procedures are implemented in an R package freely available at https://github.com/HristoInouzhe/AttractionRepulsionClustering.
Next-Active-Object prediction from Egocentric Videos
Authors: Antonino Furnari, Sebastiano Battiato, Kristen Grauman, Giovanni Maria Farinella
Date: 2019-04-10 00:00:00
Tasks: Object, Prediction

Although First Person Vision systems can sense the environment from the
user's perspective, they are generally unable to predict his intentions and
goals. Since human activities can be decomposed in terms of atomic actions and
interactions with objects, intelligent wearable systems would benefit from the
ability to anticipate user-object interactions. Even if this task is not
trivial, the First Person Vision paradigm can provide important cues to address
this challenge. We propose to exploit the dynamics of the scene to recognize
next-active-objects before an object interaction begins. We train a classifier
to discriminate trajectories leading to an object activation from all others
and forecast next-active-objects by analyzing fixed-length trajectory segments
within a temporal sliding window. The proposed method compares favorably with
respect to several baselines on the Activity of Daily Living (ADL) egocentric
dataset comprising 10 hours of videos acquired by 20 subjects while performing
unconstrained interactions with several objects.
Is Two Better than One? Effects of Multiple Agents on User Persuasion
Authors: Reshmashree B. Kantharaju, Dominic De Franco, Alison Pease, Catherine Pelachaud
Date: 2019-04-10 00:00:00

Virtual humans need to be persuasive in order to promote behaviour change in
human users. While several studies have focused on understanding the numerous
aspects that influence the degree of persuasion, most of them are limited to
dyadic interactions. In this paper, we present an evaluation study focused on
understanding the effects of multiple agents on user's persuasion. Along with
gender and status (authoritative & peer), we also look at type of focus
employed by the agent i.e., user-directed where the agent aims to persuade by
addressing the user directly and vicarious where the agent aims to persuade the
user, who is an observer, indirectly by engaging another agent in the
discussion. Participants were randomly assigned to one of the 12 conditions and
presented with a persuasive message by one or several virtual agents. A
questionnaire was used to measure perceived interpersonal attitude, credibility
and persuasion. Results indicate that credibility positively affects
persuasion. In general, multiple agent setting, irrespective of the focus, was
more persuasive than single agent setting. Although, participants favored
user-directed setting and reported it to be persuasive and had an increased
level of trust in the agents, the actual change in persuasion score reflects
that vicarious setting was the most effective in inducing behaviour change. In
addition to this, the study also revealed that authoritative agents were the
most persuasive.
A Model for Using Physiological Conditions for Proactive Tourist Recommendations
Authors: Rinita Roy, Linus W. Dietz
Date: 2019-04-10 00:00:00
Tasks: Recommendation Systems

Mobile proactive tourist recommender systems can support tourists by
recommending the best choice depending on different contexts related to herself
and the environment. In this paper, we propose to utilize wearable sensors to
gather health information about a tourist and use them for recommending tourist
activities. We discuss a range of wearable devices, sensors to infer
physiological conditions of the users, and exemplify the feasibility using a
popular self-quantification mobile app. Our main contribution then comprises a
data model to derive relations between the parameters measured by the wearable
sensors, such as heart rate, body temperature, blood pressure, and use them to
infer the physiological condition of a user. This model can then be used to
derive classes of tourist activities that determine which items should be
recommended.