pwc-archive/papers-with-abstracts huggingface dataset:
A Descriptive Study of Variable Discretization and Cost-Sensitive Logistic Regression on Imbalanced Credit Data
Authors: Lili Zhang, Herman Ray, Jennifer Priestley, Soon Tan
Date: 2018-12-28 00:00:00
Tasks: Descriptive, regression

Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit socring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 score. The results show that proper variable discretization and cost-sensitive logistic regression with the best class weights can reduce the model bias and/or variance. From the perspective of the algorithm, cost-sensitive logistic regression is beneficial for increasing the value of predictors even if they are not in their optimized forms while maintaining monotonicity. From the perspective of predictors, the variable discretization performs better than cost-sensitive logistic regression, provides more reasonable coefficient estimates for predictors which have nonlinear relationships against their empirical logit, and is robust to penalty weights on misclassifications of events and non-events determined by their apriori proportions.
A Summary of Adaptation of Techniques from Search-based Optimal Multi-Agent Path Finding Solvers to Compilation-based Approach
Authors: Pavel Surynek
Date: 2018-12-28 00:00:00
Tasks: Multi-Agent Path Finding

In the multi-agent path finding problem (MAPF) we are given a set of agents
each with respective start and goal positions. The task is to find paths for
all agents while avoiding collisions aiming to minimize an objective function.
Two such common objective functions is the sum-of-costs and the makespan. Many
optimal solvers were introduced in the past decade - two prominent categories
of solvers can be distinguished: search-based solvers and compilation-based
solvers.
  Search-based solvers were developed and tested for the sum-of-costs objective
while the most prominent compilation-based solvers that are built around
Boolean satisfiability (SAT) were designed for the makespan objective. Very
little was known on the performance and relevance of the compilation-based
approach on the sum-of-costs objective. In this paper we show how to close the
gap between these cost functions in the compilation-based approach. Moreover we
study applicability of various techniques developed for search-based solvers in
the compilation-based approach.
  A part of this paper introduces a SAT-solver that is directly aimed to solve
the sum-of-costs objective function. Using both a lower bound on the
sum-of-costs and an upper bound on the makespan, we are able to have a
reasonable number of variables in our SAT encoding. We then further improve the
encoding by borrowing ideas from ICTS, a search-based solver. Experimental
evaluation on several domains show that there are many scenarios where our new
SAT-based methods outperforms the best variants of previous sum-of-costs search
solvers - the ICTS, CBS algorithms, and ICBS algorithms.
The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength
Authors: Martin Potthast, Tim Gollub, Matthias Hagen, Benno Stein
Date: 2018-12-27 00:00:00
Tasks: Clickbait Detection, regression

Clickbait has grown to become a nuisance to social media users and social
media operators alike. Malicious content publishers misuse social media to
manipulate as many users as possible to visit their websites using clickbait
messages. Machine learning technology may help to handle this problem, giving
rise to automatic clickbait detection. To accelerate progress in this
direction, we organized the Clickbait Challenge 2017, a shared task inviting
the submission of clickbait detectors for a comparative evaluation. A total of
13 detectors have been submitted, achieving significant improvements over the
previous state of the art in terms of detection performance. Also, many of the
submitted approaches have been published open source, rendering them
reproducible, and a good starting point for newcomers. While the 2017 challenge
has passed, we maintain the evaluation system and answer to new registrations
in support of the ongoing research on better clickbait detectors.
Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction
Authors: Qiqin Dai, Henry Chopp, Emeline Pouyet, Oliver Cossairt, Marc Walton, Aggelos K. Katsaggelos
Date: 2018-12-27 00:00:00
Tasks: Binarization, Image Inpainting, Image Reconstruction

This paper presents an adaptive image sampling algorithm based on Deep Learning (DL). The adaptive sampling mask generation network is jointly trained with an image inpainting network. The sampling rate is controlled in the mask generation network, and a binarization strategy is investigated to make the sampling mask binary. Besides the image sampling and reconstruction application, we show that the proposed adaptive sampling algorithm is able to speed up raster scan processes such as the X-Ray fluorescence (XRF) image scanning process. Recently XRF laboratory-based systems have evolved to lightweight and portable instruments thanks to technological advancements in both X-Ray generation and detection. However, the scanning time of an XRF image is usually long due to the long exposures requires (e.g., $100 \mu s-1ms$ per point). We propose an XRF image inpainting approach to address the issue of long scanning time, thus speeding up the scanning process while still maintaining the possibility to reconstruct a high quality XRF image. The proposed adaptive image sampling algorithm is applied to the RGB image of the scanning target to generate the sampling mask. The XRF scanner is then driven according to the sampling mask to scan a subset of the total image pixels. Finally, we inpaint the scanned XRF image by fusing the RGB image to reconstruct the full scan XRF image. The experiments show that the proposed adaptive sampling algorithm is able to effectively sample the image and achieve a better reconstruction accuracy than that of the existing methods.
Classification of radiology reports by modality and anatomy: A comparative study
Authors: Marina Bendersky, Joy Wu, Tanveer Syeda-Mahmood
Date: 2018-12-27 00:00:00
Tasks: Anatomy, General Classification, regression

Data labeling is currently a time-consuming task that often requires expert
knowledge. In research settings, the availability of correctly labeled data is
crucial to ensure that model predictions are accurate and useful. We propose
relatively simple machine learning-based models that achieve high performance
metrics in the binary and multiclass classification of radiology reports. We
compare the performance of these algorithms to that of a data-driven approach
based on NLP, and find that the logistic regression classifier outperforms all
other models, in both the binary and multiclass classification tasks. We then
choose the logistic regression binary classifier to predict chest X-ray (CXR)/
non-chest X-ray (non-CXR) labels in reports from different datasets, unseen
during any training phase of any of the models. Even in unseen report
collections, the binary logistic regression classifier achieves average
precision values of above 0.9. Based on the regression coefficient values, we
also identify frequent tokens in CXR and non-CXR reports that are features with
possibly high predictive power.
DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems
Authors: Husheng Zhou, Wei Li, Yuankun Zhu, Yuqun Zhang, Bei Yu, Lingming Zhang, Cong Liu
Date: 2018-12-27 00:00:00
Tasks: Autonomous Driving, DNN Testing

Deep Neural Networks (DNNs) have been widely applied in many autonomous
systems such as autonomous driving. Recently, DNN testing has been intensively
studied to automatically generate adversarial examples, which inject
small-magnitude perturbations into inputs to test DNNs under extreme
situations. While existing testing techniques prove to be effective, they
mostly focus on generating digital adversarial perturbations (particularly for
autonomous driving), e.g., changing image pixels, which may never happen in
physical world. There is a critical missing piece in the literature on
autonomous driving testing: understanding and exploiting both digital and
physical adversarial perturbation generation for impacting steering decisions.
In this paper, we present DeepBillboard, a systematic physical-world testing
approach targeting at a common and practical driving scenario: drive-by
billboards. DeepBillboard is capable of generating a robust and resilient
printable adversarial billboard, which works under dynamic changing driving
conditions including viewing angle, distance, and lighting. The objective is to
maximize the possibility, degree, and duration of the steering-angle errors of
an autonomous vehicle driving by the generated adversarial billboard. We have
extensively evaluated the efficacy and robustness of DeepBillboard through
conducting both digital and physical-world experiments. Results show that
DeepBillboard is effective for various steering models and scenes. Furthermore,
DeepBillboard is sufficiently robust and resilient for generating
physical-world adversarial billboard tests for real-world driving under various
weather conditions. To the best of our knowledge, this is the first study
demonstrating the possibility of generating realistic and continuous
physical-world tests for practical autonomous driving systems.
Quantum Adiabatic Algorithm Design using Reinforcement Learning
Authors: Jian Lin, Zhong Yuan Lai, Xiaopeng Li
Date: 2018-12-27 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Quantum algorithm design plays a crucial role in exploiting the computational advantage of quantum devices. Here we develop a deep-reinforcement-learning based approach for quantum adiabatic algorithm design. Our approach is generically applicable to a class of problems with solution hard-to-find but easy-to-verify, e.g., searching and NP-complete problems. We benchmark this approach in Grover-search and 3-SAT problems, and find that the adiabatic-algorithm obtained by our RL approach leads to significant improvement in the resultant success probability. In application to Grover search, our RL-design automatically produces an adiabatic quantum algorithm that has the quadratic speedup. We find for all our studied cases that quantitatively the RL-designed algorithm has a better performance compared to the analytically constructed non-linear Hamiltonian path when the encoding Hamiltonian is solvable, and that this RL-design approach remains applicable even when the non-linear Hamiltonian path is not analytically available. In 3-SAT, we find RL-design has fascinating transferability---the adiabatic algorithm obtained by training on a specific choice of clause number leads to better performance consistently over the linear algorithm on different clause numbers. These findings suggest the applicability of reinforcement learning for automated quantum adiabatic algorithm design. Further considering the established complexity-equivalence of circuit and adiabatic quantum algorithms, we expect the RL-designed adiabatic algorithm to inspire novel circuit algorithms as well. Our approach is potentially applicable to different quantum hardwares from trapped-ions and optical-lattices to superconducting-qubit devices.
Automated Adaptation Strategies for Stream Learning
Authors: Rashid Bakirov, Bogdan Gabrys, Damien Fay
Date: 2018-12-27 00:00:00
Tasks: Model Selection

Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism.
Hyperspectral Unmixing Based on Clustered Multitask Networks
Authors: Sara Khoshsokhan, Roozbeh Rajabi, Hadi Zayyani
Date: 2018-12-27 00:00:00
Tasks: Distributed Optimization, Hyperspectral Unmixing

Hyperspectral remote sensing is a prominent research topic in data
processing. Most of the spectral unmixing algorithms are developed by adopting
the linear mixing models. Nonnegative matrix factorization (NMF) and its
developments are used widely for estimation of signatures and fractional
abundances in the SU problem. Sparsity constraints was added to NMF, and was
regularized by $ L_ {q} $ norm. In this paper, at first hyperspectral images
are clustered by fuzzy c- means method, and then a new algorithm based on
sparsity constrained distributed optimization is used for spectral unmixing. In
the proposed algorithm, a network including clusters is employed. Each pixel in
the hyperspectral images considered as a node in this network. The proposed
algorithm is optimized with diffusion LMS strategy, and then the update
equations for fractional abundance and signature matrices are obtained.
Simulation results based on defined performance metrics illustrate advantage of
the proposed algorithm in spectral unmixing of hyperspectral data compared with
other methods.
Future semantic segmentation of time-lapsed videos with large temporal displacement
Authors: Talha Siddiqui, Samarth Bharadwaj
Date: 2018-12-27 00:00:00
Tasks: Segmentation, Semantic Segmentation, Video Understanding

An important aspect of video understanding is the ability to predict the
evolution of its content in the future. This paper presents a future frame
semantic segmentation technique for predicting semantic masks of the current
and future frames in a time-lapsed video. We specifically focus on time-lapsed
videos with large temporal displacement to highlight the model's ability to
capture large motions in time. We first introduce a unique semantic
segmentation prediction dataset with over 120,000 time-lapsed sky-video frames
and all corresponding semantic masks captured over a span of five years in
North America region. The dataset has immense practical value for cloud cover
analysis, which are treated as non-rigid objects of interest. %Here the model
provides both semantic segmentation of cloud region and solar irradiance
emitted from a region from the sky-videos. Next, our proposed recurrent network
architecture departs from existing trend of using temporal convolutional
networks (TCN) (or feed-forward networks), by explicitly learning an internal
representations for the evolution of video content with time. Experimental
evaluation shows an improvement of mean IoU over TCNs in the segmentation task
by 10.8% for 10 mins (21% over 60 mins) ahead of time predictions. Further, our
model simultaneously measures both the current and future solar irradiance from
the same video frames with a normalized-MAE of 10.5% over two years. These
results indicate that recurrent memory networks with attention mechanism are
able to capture complex advective and diffused flow characteristic of dense
fluids even with sparse temporal sampling and are more suitable for future
frame prediction tasks for longer duration videos.
Can Image Enhancement be Beneficial to Find Smoke Images in Laparoscopic Surgery?
Authors: Congcong Wang, Vivek Sharma, Yu Fan, Faouzi Alaya Cheikh, Azeddine Beghdadi, Ole Jacob Elle, Rainer Stiefelhagen
Date: 2018-12-27 00:00:00
Tasks: General Classification, Image Enhancement, Single Particle Analysis

Laparoscopic surgery has a limited field of view. Laser ablation in a
laproscopic surgery causes smoke, which inevitably influences the surgeon's
visibility. Therefore, it is of vital importance to remove the smoke, such that
a clear visualization is possible. In order to employ a desmoking technique,
one needs to know beforehand if the image contains smoke or not, to this date,
there exists no accurate method that could classify the smoke/non-smoke images
completely. In this work, we propose a new enhancement method which enhances
the informative details in the RGB images for discrimination of smoke/non-smoke
images. Our proposed method utilizes weighted least squares optimization
framework~(WLS). For feature extraction, we use statistical features based on
bivariate histogram distribution of gradient magnitude~(GM) and Laplacian of
Gaussian~(LoG). We then train a SVM classifier with binary smoke/non-smoke
classification task. We demonstrate the effectiveness of our method on Cholec80
dataset. Experiments using our proposed enhancement method show promising
results with improvements of 4\% in accuracy and 4\% in F1-Score over the
baseline performance of RGB images. In addition, our approach improves over the
saturation histogram based classification methodologies Saturation
Analysis~(SAN) and Saturation Peak Analysis~(SPA) by 1/5\% and 1/6\% in
accuracy/F1-Score metrics.
Topological Constraints on Homeomorphic Auto-Encoding
Authors: Pim de Haan, Luca Falorsi
Date: 2018-12-27 00:00:00
Tasks: Representation Learning

When doing representation learning on data that lives on a known non-trivial
manifold embedded in high dimensional space, it is natural to desire the
encoder to be homeomorphic when restricted to the manifold, so that it is
bijective and continuous with a continuous inverse. Using topological
arguments, we show that when the manifold is non-trivial, the encoder must be
globally discontinuous and propose a universal, albeit impractical,
construction. In addition, we derive necessary constraints which need to be
satisfied when designing manifold-specific practical encoders. These are used
to analyse candidates for a homeomorphic encoder for the manifold of 3D
rotations $SO(3)$.
Evaluating Generative Adversarial Networks on Explicitly Parameterized Distributions
Authors: Shayne O'Brien, Matt Groh, Abhimanyu Dubey
Date: 2018-12-27 00:00:00

The true distribution parameterizations of commonly used image datasets are
inaccessible. Rather than designing metrics for feature spaces with unknown
characteristics, we propose to measure GAN performance by evaluating on
explicitly parameterized, synthetic data distributions. As a case study, we
examine the performance of 16 GAN variants on six multivariate distributions of
varying dimensionalities and training set sizes. In this learning environment,
we observe that: GANs exhibit similar performance trends across
dimensionalities; learning depends on the underlying distribution and its
complexity; the number of training samples can have a large impact on
performance; evaluation and relative comparisons are metric-dependent; diverse
sets of hyperparameters can produce a "best" result; and some GANs are more
robust to hyperparameter changes than others. These observations both
corroborate findings of previous GAN evaluation studies and make novel
contributions regarding the relationship between size, complexity, and GAN
performance.
Semantic Driven Multi-Camera Pedestrian Detection
Authors: Alejandro López-Cifuentes, Marcos Escudero-Viñolo, Jesús Bescós, Pablo Carballeira
Date: 2018-12-27 00:00:00
Tasks: Attribute, global-optimization, Pedestrian Detection, Semantic Segmentation

In the current worldwide situation, pedestrian detection has reemerged as a pivotal tool for intelligent video-based systems aiming to solve tasks such as pedestrian tracking, social distancing monitoring or pedestrian mass counting. Pedestrian detection methods, even the top performing ones, are highly sensitive to occlusions among pedestrians, which dramatically degrades their performance in crowded scenarios. The generalization of multi-camera set-ups permits to better confront occlusions by combining information from different viewpoints. In this paper, we present a multi-camera approach to globally combine pedestrian detections leveraging automatically extracted scene context. Contrarily to the majority of the methods of the state-of-the-art, the proposed approach is scene-agnostic, not requiring a tailored adaptation to the target scenario\textemdash e.g., via fine-tunning. This noteworthy attribute does not require \textit{ad hoc} training with labelled data, expediting the deployment of the proposed method in real-world situations. Context information, obtained via semantic segmentation, is used 1) to automatically generate a common Area of Interest for the scene and all the cameras, avoiding the usual need of manually defining it; and 2) to obtain detections for each camera by solving a global optimization problem that maximizes coherence of detections both in each 2D image and in the 3D scene. This process yields tightly-fitted bounding boxes that circumvent occlusions or miss-detections. Experimental results on five publicly available datasets show that the proposed approach outperforms state-of-the-art multi-camera pedestrian detectors, even some specifically trained on the target scenario, signifying the versatility and robustness of the proposed method without requiring ad-hoc annotations nor human-guided configuration.
3D Point Capsule Networks
Authors: Yongheng Zhao, Tolga Birdal, Haowen Deng, Federico Tombari
Date: 2018-12-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Feature Matching, 3D Geometry Perception, 3D Object Classification, 3D Object Reconstruction, 3D Part Segmentation, 3D Point Cloud Matching, 3D Shape Generation, 3D Shape Representation, General Classification, Object

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D latent space deployed by our approach bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement.