pwc-archive/papers-with-abstracts huggingface dataset:
Detecting GAN generated errors
Authors: Xiru Zhu, Fengdi Che, Tianzi Yang, Tzuyang Yu, David Meger, Gregory Dudek
Date: 2019-12-02 00:00:00

Despite an impressive performance from the latest GAN for generating hyper-realistic images, GAN discriminators have difficulty evaluating the quality of an individual generated sample. This is because the task of evaluating the quality of a generated image differs from deciding if an image is real or fake. A generated image could be perfect except in a single area but still be detected as fake. Instead, we propose a novel approach for detecting where errors occur within a generated image. By collaging real images with generated images, we compute for each pixel, whether it belongs to the real distribution or generated distribution. Furthermore, we leverage attention to model long-range dependency; this allows detection of errors which are reasonable locally but not holistically. For evaluation, we show that our error detection can act as a quality metric for an individual image, unlike FID and IS. We leverage Improved Wasserstein, BigGAN, and StyleGAN to show a ranking based on our metric correlates impressively with FID scores. Our work opens the door for better understanding of GAN and the ability to select the best samples from a GAN model.
Anomaly Detection in Particulate Matter Sensor using Hypothesis Pruning Generative Adversarial Network
Authors: YeongHyeon Park, Won Seok Park, Yeong Beom Kim
Date: 2019-12-02 00:00:00
Tasks: Anomaly Detection, Generative Adversarial Network

World Health Organization (WHO) provides the guideline for managing the Particulate Matter (PM) level because when the PM level is higher, it threats the human health. For managing PM level, the procedure for measuring PM value is needed firstly. We use Tapered Element Oscillating Microbalance (TEOM)-based PM measuring sensors because it shows higher cost-effectiveness than Beta Attenuation Monitor (BAM)-based sensor. However, TEOM-based sensor has higher probability of malfunctioning than BAM-based sensor. In this paper, we call the overall malfunction as an anomaly, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that named as Hypothesis Pruning Generative Adversarial Network (HP-GAN). We experimentally compare the several anomaly detection architectures to certify ours performing better.
An Attention-Based Speaker Naming Method for Online Adaptation in Non-Fixed Scenarios
Authors: Jungwoo Pyo, Joohyun Lee, Youngjune Park, Tien-Cuong Bui, Sang Kyun Cha
Date: 2019-12-02 00:00:00
Tasks: Video Summarization

A speaker naming task, which finds and identifies the active speaker in a certain movie or drama scene, is crucial for dealing with high-level video analysis applications such as automatic subtitle labeling and video summarization. Modern approaches have usually exploited biometric features with a gradient-based method instead of rule-based algorithms. In a certain situation, however, a naive gradient-based method does not work efficiently. For example, when new characters are added to the target identification list, the neural network needs to be frequently retrained to identify new people and it causes delays in model preparation. In this paper, we present an attention-based method which reduces the model setup time by updating the newly added data via online adaptation without a gradient update process. We comparatively analyzed with three evaluation metrics(accuracy, memory usage, setup time) of the attention-based method and existing gradient-based methods under various controlled settings of speaker naming. Also, we applied existing speaker naming models and the attention-based model to real video to prove that our approach shows comparable accuracy to the existing state-of-the-art models and even higher accuracy in some cases.
Detecting mechanical loosening of total hip replacement implant from plain radiograph using deep convolutional neural network
Authors: Alireza Borjali, Antonia F. Chen, Orhun K. Muratoglu, Mohammad A. Morid, Kartik M. Varadarajan
Date: 2019-12-02 00:00:00
Tasks: Decision Making, Diagnostic, Specificity

Plain radiography is widely used to detect mechanical loosening of total hip replacement (THR) implants. Currently, radiographs are assessed manually by medical professionals, which may be prone to poor inter and intra observer reliability and low accuracy. Furthermore, manual detection of mechanical loosening of THR implants requires experienced clinicians who might not always be readily available, potentially resulting in delayed diagnosis. In this study, we present a novel, fully automatic and interpretable approach to detect mechanical loosening of THR implants from plain radiographs using deep convolutional neural network (CNN). We trained a CNN on 40 patients anteroposterior hip x rays using five fold cross validation and compared its performance with a high volume board certified orthopaedic surgeon (AFC). To increase the confidence in the machine outcome, we also implemented saliency maps to visualize where the CNN looked at to make a diagnosis. CNN outperformed the orthopaedic surgeon in diagnosing mechanical loosening of THR implants achieving significantly higher sensitively (0.94) than the orthopaedic surgeon (0.53) with the same specificity (0.96). The saliency maps showed that the CNN looked at clinically relevant features to make a diagnosis. Such CNNs can be used for automatic radiologic assessment of mechanical loosening of THR implants to supplement the practitioners decision making process, increasing their diagnostic accuracy, and freeing them to engage in more patient centric care.
Augmented Reality for Human-Swarm Interaction in a Swarm-Robotic Chemistry Simulation
Authors: Sumeet Batra, John Klingner, Nikolaus Correll
Date: 2019-12-02 00:00:00

We present a method to register individual members of a robotic swarm in an augmented reality display while showing relevant information about swarm dynamics to the user that would be otherwise hidden. Individual swarm members and clusters of the same group are identified by their color, and by blinking at a specific time interval that is distinct from the time interval at which their neighbors blink. We show that this problem is an instance of the graph coloring problem, which can be solved in a distributed manner in O(log(n)) time. We demonstrate our approach using a swarm chemistry simulation in which robots simulate individual atoms that form molecules following the rules of chemistry. Augmented reality is then used to display information about the internal state of individual swarm members as well as their topological relationship, corresponding to molecular bonds.
Applying Knowledge Transfer for Water Body Segmentation in Peru
Authors: Jessenia Gonzalez, Debjani Bhowmick, Cesar Beltran, Kris Sankaran, Yoshua Bengio
Date: 2019-12-02 00:00:00
Tasks: Transfer Learning

In this work, we present the application of convolutional neural networks for segmenting water bodies in satellite images. We first use a variant of the U-Net model to segment rivers and lakes from very high-resolution images from Peru. To circumvent the issue of scarce labelled data, we investigate the applicability of a knowledge transfer-based model that learns the mapping from high-resolution labelled images and combines it with the very high-resolution mapping so that better segmentation can be achieved. We train this model in a single process, end-to-end. Our preliminary results show that adding the information from the available high-resolution images does not help out-of-the-box, and in fact worsen results. This leads us to infer that the high-resolution data could be from a different distribution, and its addition leads to increased variance in our results.
Training Distributed Deep Recurrent Neural Networks with Mixed Precision on GPU Clusters
Authors: Alexey Svyatkovskiy, Julian Kates-Harbeck, William Tang
Date: 2019-11-30 00:00:00
Tasks: GPU, Time Series, Time Series Analysis

In this paper, we evaluate training of deep recurrent neural networks with half-precision floats. We implement a distributed, data-parallel, synchronous training algorithm by integrating TensorFlow and CUDA-aware MPI to enable execution across multiple GPU nodes and making use of high-speed interconnects. We introduce a learning rate schedule facilitating neural network convergence at up to $O(100)$ workers. Strong scaling tests performed on clusters of NVIDIA Pascal P100 GPUs show linear runtime and logarithmic communication time scaling for both single and mixed precision training modes. Performance is evaluated on a scientific dataset taken from the Joint European Torus (JET) tokamak, containing multi-modal time series of sensory measurements leading up to deleterious events called plasma disruptions, and the benchmark Large Movie Review Dataset~\cite{imdb}. Half-precision significantly reduces memory and network bandwidth, allowing training of state-of-the-art models with over 70 million trainable parameters while achieving a comparable test set performance as single precision.
Active Search for Nearest Neighbors
Authors: Hayoung Um, Heeyoul Choi
Date: 2019-12-01 00:00:00

In pattern recognition or machine learning, it is a very fundamental task to find nearest neighbors of a given point. All the methods for the task work basically by comparing the given point to all the points in the data set. That is why the computational cost increases with the number of data points. However, the human visual system seems to work in a different way. When the human visual system tries to find the neighbors of one point on a map, it directly focuses on the area around the point and actively searches the neighbors by looking or zooming in and out around the point. In this paper, we propose an innovative search method for nearest neighbors, which seems very similar to how human visual system works on the task.
Real-time Travel Time Estimation Using Matrix Factorization
Authors: Ebrahim Badrestani, Behnam Bahrak, Ali Elahi, Adib Faramarzi, Pouria Golshanrad, Amin Karimi Monsefi, Hamid Mahini, Armin Zirak
Date: 2019-12-01 00:00:00
Tasks: Travel Time Estimation

Estimating the travel time of any route is of great importance for trip planners, traffic operators, online taxi dispatching and ride-sharing platforms, and navigation provider systems. With the advance of technology, many traveling cars, including online taxi dispatch systems' vehicles are equipped with Global Positioning System (GPS) devices that can report the location of the vehicle every few seconds. This paper uses GPS data and the Matrix Factorization techniques to estimate the travel times on all road segments and time intervals simultaneously. We aggregate GPS data into a matrix, where each cell of the original matrix contains the average vehicle speed for a segment and a specific time interval. One of the problems with this matrix is its high sparsity. We use Alternating Least Squares (ALS) method along with a regularization term to factorize the matrix. Since this approach can solve the sparsity problem that arises from the absence of cars in many road segments in a specific time interval, matrix factorization is suitable for estimating the travel time. Our comprehensive evaluation results using real data provided by one of the largest online taxi dispatching systems in Iran, shows the strength of our proposed method.
Time-Guided High-Order Attention Model of Longitudinal Heterogeneous Healthcare Data
Authors: Yi Huang, Xiaoshan Yang, Changsheng Xu
Date: 2019-11-28 00:00:00
Tasks: Management, Mortality Prediction, Vocal Bursts Intensity Prediction

Due to potential applications in chronic disease management and personalized healthcare, the EHRs data analysis has attracted much attention of both researchers and practitioners. There are three main challenges in modeling longitudinal and heterogeneous EHRs data: heterogeneity, irregular temporality and interpretability. A series of deep learning methods have made remarkable progress in resolving these challenges. Nevertheless, most of existing attention models rely on capturing the 1-order temporal dependencies or 2-order multimodal relationships among feature elements. In this paper, we propose a time-guided high-order attention (TGHOA) model. The proposed method has three major advantages. (1) It can model longitudinal heterogeneous EHRs data via capturing the 3-order correlations of different modalities and the irregular temporal impact of historical events. (2) It can be used to identify the potential concerns of medical features to explain the reasoning process of the healthcare model. (3) It can be easily expanded into cases with more modalities and flexibly applied in different prediction tasks. We evaluate the proposed method in two tasks of mortality prediction and disease ranking on two real world EHRs datasets. Extensive experimental results show the effectiveness of the proposed model.
Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study
Authors: Fitash Ul Haq, Donghwan Shin, Shiva Nejati, Lionel Briand
Date: 2019-11-28 00:00:00
Tasks: DNN Testing

There is a growing body of research on developing testing techniques for Deep Neural Networks (DNN). We distinguish two general modes of testing for DNNs: Offline testing where DNNs are tested as individual units based on test datasets obtained independently from the DNNs under test, and online testing where DNNs are embedded into a specific application and tested in a close-loop mode in interaction with the application environment. In addition, we identify two sources for generating test datasets for DNNs: Datasets obtained from real-life and datasets generated by simulators. While offline testing can be used with datasets obtained from either sources, online testing is largely confined to using simulators since online testing within real-life applications can be time-consuming, expensive and dangerous. In this paper, we study the following two important questions aiming to compare test datasets and testing modes for DNNs: First, can we use simulator-generated data as a reliable substitute to real-world data for the purpose of DNN testing? Second, how do online and offline testing results differ and complement each other? Though these questions are generally relevant to all autonomous systems, we study them in the context of automated driving systems where, as study subjects, we use DNNs automating end-to-end control of cars' steering actuators. Our results show that simulator-generated datasets are able to yield DNN prediction errors that are similar to those obtained by testing DNNs with real-life datasets. Further, offline testing is more optimistic than online testing as many safety violations identified by online testing could not be identified by offline testing, while large prediction errors generated by offline testing always led to severe safety violations detectable by online testing.
Long Short-Term Network Based Unobtrusive Perceived Workload Monitoring with Consumer Grade Smartwatches in the Wild
Authors: Deniz Ekiz, Yekta Said Can, Cem Ersoy
Date: 2019-11-30 00:00:00
Tasks: Heart Rate Variability

Continuous high perceived workload has a negative impact on the individual's well-being. Prior works focused on detecting the workload with medical-grade wearable systems in the restricted settings, and the effect of applying deep learning techniques for perceived workload detection in the wild settings is not investigated. We present an unobtrusive, comfortable, pervasive and affordable Long Short-Term Memory Network based continuous workload monitoring system based on a smartwatch application that monitors the perceived workload of individuals in the wild. We make use of modern consumer-grade smartwatches. We have recorded physiological data from daily life with perceived workload questionnaires from subjects in their real-life environments over a month. The model was trained and evaluated with the daily-life physiological data coming from different days which makes it robust to daily changes in the heart rate variability, that we use with accelerometer features to asses low and high workload. Our system has the capability of removing motion-related artifacts and detecting perceived workload by using traditional and deep classifiers. We discussed the problems related to in the wild applications with the consumer-grade smartwatches. We showed that Long Short-Term Memory Network outperforms traditional classifiers on discrimination of low and high workload with smartwatches in the wild.
The Topology of Mutated Driver Pathways
Authors: Raouf Dridi, Hedayat Alghassi, Maen Obeidat, Sridhar Tayur
Date: 2019-11-30 00:00:00

Much progress has been made, and continues to be made, towards identifying candidate mutated driver pathways in cancer. However, no systematic approach to understanding how candidate pathways relate to each other for a given cancer (such as Acute myeloid leukemia), and how one type of cancer may be similar or different from another with regard to their respective pathways (Acute myeloid leukemia vs. Glioblastoma multiforme for instance), has emerged thus far. Our work attempts to contribute to the understanding of {\em space of pathways} through a novel topological framework. We illustrate our approach, using mutation data (obtained from TCGA) of two types of tumors: Acute myeloid leukemia (AML) and Glioblastoma multiforme (GBM). We find that the space of pathways for AML is homotopy equivalent to a sphere, while that of GBM is equivalent to a genus-2 surface. We hope to trigger new types of questions (i.e., allow for novel kinds of hypotheses) towards a more comprehensive grasp of cancer.
Quantized deep learning models on low-power edge devices for robotic systems
Authors: Anugraha Sinha, Naveen Kumar, Murukesh Mohanan, MD Muhaimin Rahman, Yves Quemener, Amina Mim, Suzana Ilić
Date: 2019-11-30 00:00:00

In this work, we present a quantized deep neural network deployed on a low-power edge device, inferring learned motor-movements of a suspended robot in a defined space. This serves as the fundamental building block for the original setup, a robotic system for farms or greenhouses aimed at a wide range of agricultural tasks. Deep learning on edge devices and its implications could have a substantial impact on farming systems in the developing world, leading not only to sustainable food production and income, but also increased data privacy and autonomy.
Mix and Match: Markov Chains & Mixing Times for Matching in Rideshare
Authors: Michael J. Curry, John P. Dickerson, Karthik Abinav Sankararaman, Aravind Srinivasan, Yuhao Wan, Pan Xu
Date: 2019-11-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), valid

Rideshare platforms such as Uber and Lyft dynamically dispatch drivers to match riders' requests. We model the dispatching process in rideshare as a Markov chain that takes into account the geographic mobility of both drivers and riders over time. Prior work explores dispatch policies in the limit of such Markov chains; we characterize when this limit assumption is valid, under a variety of natural dispatch policies. We give explicit bounds on convergence in general, and exact (including constants) convergence rates for special cases. Then, on simulated and real transit data, we show that our bounds characterize convergence rates -- even when the necessary theoretical assumptions are relaxed. Additionally these policies compare well against a standard reinforcement learning algorithm which optimizes for profit without any convergence properties.
Scalable Graph Algorithms
Authors: Christian Schulz
Date: 2019-11-30 00:00:00

Processing large complex networks recently attracted considerable interest. Complex graphs are useful in a wide range of applications from technological networks to biological systems like the human brain. Sometimes these networks are composed of billions of entities that give rise to emerging properties and structures. Analyzing these structures aids us in gaining new insights about our surroundings. As huge networks become abundant, there is a need for scalable algorithms to perform analysis. A prominent example is the PageRank algorithm, which is one of the measures used by web search engines such as Google to rank web pages displayed to the user. In order to find these patterns, massive amounts of data have to be acquired and processed. Designing and evaluating scalable graph algorithms to handle these data sets is a crucial task on the road to understanding the underlying systems. This habilitation thesis is a summary a broad spectrum of scalable graph algorithms that I developed over the last six years with many coauthors. In general, this research is based on four pillars: multilevel algorithms, practical kernelization, parallelization and memetic algorithms that are highly interconnected. Experiments conducted indicate that our algorithms find better solutions and/or are much more scalable than the previous state-of-the-art.
Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features
Authors: Ulysse Côté-Allard, Evan Campbell, Angkoon Phinyomark, François Laviolette, Benoit Gosselin, Erik Scheme
Date: 2019-11-30 00:00:00
Tasks: Feature Engineering, Gesture Recognition, Topological Data Analysis

The research in myoelectric control systems primarily focuses on extracting discriminative representations from the electromyographic (EMG) signal by designing handcrafted features. Recently, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition. The adoption of these techniques slowly shifts the focus from feature engineering to feature learning. However, the black-box nature of deep learning makes it hard to understand the type of information learned by the network and how it relates to handcrafted features. Additionally, due to the high variability in EMG recordings between participants, deep features tend to generalize poorly across subjects using standard training methods. Consequently, this work introduces a new multi-domain learning algorithm, named ADANN, which significantly enhances (p=0.00004) inter-subject classification accuracy by an average of 19.40% compared to standard training. Using ADANN-generated features, the main contribution of this work is to provide the first topological data analysis of EMG-based gesture recognition for the characterisation of the information encoded within a deep network, using handcrafted features as landmarks. This analysis reveals that handcrafted features and the learned features (in the earlier layers) both try to discriminate between all gestures, but do not encode the same information to do so. Furthermore, using convolutional network visualization techniques reveal that learned features tend to ignore the most activated channel during gesture contraction, which is in stark contrast with the prevalence of handcrafted features designed to capture amplitude information. Overall, this work paves the way for hybrid feature sets by providing a clear guideline of complementary information encoded within learned and handcrafted features.