pwc-archive/papers-with-abstracts huggingface dataset:
Deep Compressed Pneumonia Detection for Low-Power Embedded Devices
Authors: Hongjia Li, Sheng Lin, Ning Liu, Caiwen Ding, Yanzhi Wang
Date: 2019-11-04 00:00:00
Tasks: Pneumonia Detection

Deep neural networks (DNNs) have been expanded into medical fields and triggered the revolution of some medical applications by extracting complex features and achieving high accuracy and performance, etc. On the contrast, the large-scale network brings high requirements of both memory storage and computation resource, especially for portable medical devices and other embedded systems. In this work, we first train a DNN for pneumonia detection using the dataset provided by RSNA Pneumonia Detection Challenge. To overcome hardware limitation for implementing large-scale networks, we develop a systematic structured weight pruning method with filter sparsity, column sparsity and combined sparsity. Experiments show that we can achieve up to 36x compression ratio compared to the original model with 106 layers, while maintaining no accuracy degradation. We evaluate the proposed methods on an embedded low-power device, Jetson TX2, and achieve low power usage and high energy efficiency.
ODE-Inspired Analysis for the Biological Version of Oja's Rule in Solving Streaming PCA
Authors: Chi-Ning Chou, Mien Brabeeba Wang
Date: 2019-11-04 00:00:00

Oja's rule [Oja, Journal of mathematical biology 1982] is a well-known biologically-plausible algorithm using a Hebbian-type synaptic update rule to solve streaming principal component analysis (PCA). Computational neuroscientists have known that this biological version of Oja's rule converges to the top eigenvector of the covariance matrix of the input in the limit. However, prior to this work, it was open to prove any convergence rate guarantee. In this work, we give the first convergence rate analysis for the biological version of Oja's rule in solving streaming PCA. Moreover, our convergence rate matches the information theoretical lower bound up to logarithmic factors and outperforms the state-of-the-art upper bound for streaming PCA. Furthermore, we develop a novel framework inspired by ordinary differential equations (ODE) to analyze general stochastic dynamics. The framework abandons the traditional step-by-step analysis and instead analyzes a stochastic dynamic in one-shot by giving a closed-form solution to the entire dynamic. The one-shot framework allows us to apply stopping time and martingale techniques to have a flexible and precise control on the dynamic. We believe that this general framework is powerful and should lead to effective yet simple analysis for a large class of problems with stochastic dynamics.
Modelling bid-ask spread conditional distributions using hierarchical correlation reconstruction
Authors: Jarosław Duda, Robert Syrek, Henryk Gurgul
Date: 2019-11-04 00:00:00

While we would like to predict exact values, available incomplete information is rarely sufficient - usually allowing only to predict conditional probability distributions. This article discusses hierarchical correlation reconstruction (HCR) methodology for such prediction on example of usually unavailable bid-ask spreads, predicted from more accessible data like closing price, volume, high/low price, returns. In HCR methodology we first normalize marginal distributions to nearly uniform like in copula theory. Then we model (joint) densities as linear combinations of orthonormal polynomials, getting its decomposition into (mixed) moments. Then here we model each moment (separately) of predicted variable as a linear combination of mixed moments of known variables using least squares linear regression - getting accurate description with interpretable coefficients describing linear relations between moments. Combining such predicted moments we get predicted density as a polynomial, for which we can e.g. calculate expected value, but also variance to evaluate uncertainty of such prediction, or we can use the entire distribution e.g. for more accurate further calculations or generating random values. There were performed 10-fold cross-validation log-likelihood tests for 22 DAX companies, leading to very accurate predictions, especially when using individual models for each company as there were found large differences between their behaviors. Additional advantage of the discussed methodology is being computationally inexpensive, finding and evaluation a model with hundreds of parameters and thousands of data points takes a second on a laptop.
Unimodal-uniform Constrained Wasserstein Training for Medical Diagnosis
Authors: Xiaofeng Liu, Xu Han, Yukai Qiao, Yi Ge, Lu Jun
Date: 2019-11-03 00:00:00
Tasks: Binary Classification, Medical Diagnosis

The labels in medical diagnosis task are usually discrete and successively distributed. For example, the Diabetic Retinopathy Diagnosis (DR) involves five health risk levels: no DR (0), mild DR (1), moderate DR (2), severe DR (3) and proliferative DR (4). This labeling system is common for medical disease. Previous methods usually construct a multi-binary-classification task or propose some re-parameter schemes in the output unit. In this paper, we target on this task from the perspective of loss function. More specifically, the Wasserstein distance is utilized as an alternative, explicitly incorporating the inter-class correlations by pre-defining its ground metric. Then, the ground metric which serves as a linear, convex or concave increasing function w.r.t. the Euclidean distance in a line is explored from an optimization perspective. Meanwhile, this paper also proposes of constructing the smoothed target labels that model the inlier and outlier noises by using a unimodal-uniform mixture distribution. Different from the one-hot setting, the smoothed label endues the computation of Wasserstein distance with more challenging features. With either one-hot or smoothed target label, this paper systematically concludes the practical closed-form solution. We evaluate our method on several medical diagnosis tasks (e.g., Diabetic Retinopathy and Ultrasound Breast dataset) and achieve state-of-the-art performance.
Nesterov Acceleration of Alternating Least Squares for Canonical Tensor Decomposition: Momentum Step Size Selection and Restart Mechanisms
Authors: Drew Mitchell, Nan Ye, Hans De Sterck
Date: 2018-10-13 00:00:00
Tasks: Tensor Decomposition

We present Nesterov-type acceleration techniques for Alternating Least Squares (ALS) methods applied to canonical tensor decomposition. While Nesterov acceleration turns gradient descent into an optimal first-order method for convex problems by adding a momentum term with a specific weight sequence, a direct application of this method and weight sequence to ALS results in erratic convergence behaviour. This is so because the tensor decomposition problem is non-convex and ALS is accelerated instead of gradient descent. Instead, we consider various restart mechanisms and suitable choices of momentum weights that enable effective acceleration. Our extensive empirical results show that the Nesterov-accelerated ALS methods with restart can be dramatically more efficient than the stand-alone ALS or Nesterov accelerated gradient methods, when problems are ill-conditioned or accurate solutions are desired. The resulting methods perform competitively with or superior to existing acceleration methods for ALS, including ALS acceleration by NCG, NGMRES, or LBFGS, and additionally enjoy the benefit of being much easier to implement. We also compare with Nesterov-type updates where the momentum weight is determined by a line search, which are equivalent or closely related to existing line search methods for ALS. On a large and ill-conditioned 71$\times$1000$\times$900 tensor consisting of readings from chemical sensors to track hazardous gases, the restarted Nesterov-ALS method shows desirable robustness properties and outperforms any of the existing methods by a large factor. There is clear potential for extending our Nesterov-type acceleration approach to accelerating other optimization algorithms than ALS applied to other non-convex problems, such as Tucker tensor decomposition. Our Matlab code is available at https://github.com/hansdesterck/nonlinear-preconditioning-for-optimization.
Fuzzy Inference Procedure for Intelligent and Automated Control of Refrigerant Charging
Authors: Issam Damaj, Jean Saade, Hala Al-Faisal, Hassan Diab
Date: 2019-11-02 00:00:00
Tasks: Decision Making

Fuzzy logic controllers are readily customizable in natural language terms and can effectively deal with nonlinearities and uncertainties in control systems. This paper presents an intelligent and automated fuzzy control procedure for the refrigerant charging of refrigerators. The elements that affect the experimental charging and the optimization of the performance of refrigerators are fuzzified and used in an inference model. The objective is to represent the intelligent behavior of a human tester and ultimately make the developed model available for the use in an automated data acquisition, monitoring, and decision-making system. The proposed system is capable of determining the needed amount of refrigerant in the shortest possible time. The system automates the refrigerant charging and performance testing of parallel units. The system is built using data acquisition systems from National Instruments and programmed under LabVIEW. The developed fuzzy models, and their testing results, are evaluated according to their compatibility with the principles that govern the intelligent behavior of human experts when performing the refrigerant-charging process. In addition, comparisons of the fuzzy models with classical inference models are presented. The obtained results confirm that the proposed fuzzy controllers outperform traditional crisp controllers and provide major test time and energy savings. The paper includes thorough discussions, analysis, and evaluation.
Variational Autoencoders for Generative Modelling of Water Cherenkov Detectors
Authors: Abhishek Abhishek, Wojciech Fedorko, Patrick de Perio, Nicholas Prouse, Julian Z. Ding
Date: 2019-11-01 00:00:00
Tasks: Synthetic Data Generation

Matter-antimatter asymmetry is one of the major unsolved problems in physics that can be probed through precision measurements of charge-parity symmetry violation at current and next-generation neutrino oscillation experiments. In this work, we demonstrate the capability of variational autoencoders and normalizing flows to approximate the generative distribution of simulated data for water Cherenkov detectors commonly used in these experiments. We study the performance of these methods and their applicability for semi-supervised learning and synthetic data generation.
Road Surface Friction Prediction Using Long Short-Term Memory Neural Network Based on Historical Data
Authors: Ziyuan Pu, Shuo Wang, Chenglong Liu, Zhiyong Cui, Yinhai Wang
Date: 2019-11-01 00:00:00
Tasks: Decision Making, Friction, Prediction, Time Series, Time Series Analysis

Road surface friction significantly impacts traffic safety and mobility. A precise road surface friction prediction model can help to alleviate the influence of inclement road conditions on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity. Most related previous studies are laboratory-based methods that are difficult for practical implementation. Moreover, in other data-driven methods, the demonstrated time-series features of road surface conditions have not been considered. This study employed a Long-Short Term Memory (LSTM) neural network to develop a data-driven road surface friction prediction model based on historical data. The proposed prediction model outperformed the other baseline models in terms of the lowest value of predictive performance measurements. The influence of the number of time-lags and the predicting time interval on predictive accuracy was analyzed. In addition, the influence of adding road surface water thickness, road surface temperature and air temperature on predictive accuracy also were investigated. The findings of this study can support road maintenance strategy development and decision making, thus mitigating the impact of inclement road conditions on traffic mobility and safety. Future work includes a modified LSTM-based prediction model development by accommodating flexible time intervals between time-lags.
The non-capacitor model of leaky integrate-and-fire $VO_2$ neuron with the thermal mechanism of the membrane potential
Authors: A. A. Velichko, M. A. Belyaev, D. V. Ryabokon, S. D. Khanin
Date: 2019-10-07 00:00:00

The study presents a numerical model of leaky integrate-and-fire neuron created on the basis of $VO_2$ switch. The analogue of the membrane potential in the model is the temperature of the switch channel, and the action potential from neighbouring neurons propagates along the substrate in the form of thermal pulses. We simulated the operation of three neurons and demonstrated that the total effect happens due to interference of thermal waves in the region of the neuron switching channel. The thermal mechanism of the threshold function operates due to the effect of electrical switching, and the magnitude (temperature) of the threshold can vary by external voltage. The neuron circuit does not contain capacitor, making it possible to produce a network with a high density of components, and has the potential for 3D integration due to the thermal mechanism of neurons interaction.
Towards Human Centered AutoML
Authors: Florian Pfisterer, Janek Thomas, Bernd Bischl
Date: 2019-11-06 00:00:00
Tasks: AutoML, Position

Building models from data is an integral part of the majority of data science workflows. While data scientists are often forced to spend the majority of the time available for a given project on data cleaning and exploratory analysis, the time available to practitioners to build actual models from data is often rather short due to time constraints for a given project. AutoML systems are currently rising in popularity, as they can build powerful models without human oversight. In this position paper, we aim to discuss the impact of the rising popularity of such systems and how a user-centered interface for such systems could look like. More importantly, we also want to point out features that are currently missing in those systems and start to explore better usability of such systems from a data-scientists perspective.
Reversible Adversarial Attack based on Reversible Image Transformation
Authors: Zhaoxia Yin, Hua Wang, Li Chen, Jie Wang, Weiming Zhang
Date: 2019-11-06 00:00:00
Tasks: Adversarial Attack, Image Restoration

In order to prevent illegal or unauthorized access of image data such as human faces and ensure legitimate users can use authorization-protected data, reversible adversarial attack technique is rise. Reversible adversarial examples (RAE) get both attack capability and reversibility at the same time. However, the existing technique can not meet application requirements because of serious distortion and failure of image recovery when adversarial perturbations get strong. In this paper, we take advantage of Reversible Image Transformation technique to generate RAE and achieve reversible adversarial attack. Experimental results show that proposed RAE generation scheme can ensure imperceptible image distortion and the original image can be reconstructed error-free. What's more, both the attack ability and the image quality are not limited by the perturbation amplitude.
Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography
Authors: Sarah Leclerc, Erik Smistad, João Pedrosa, Andreas Østvik, Frederic Cervenansky, Florian Espinosa, Torvald Espeland, Erik Andreas Rye Berg, Pierre-Marc Jodoin, Thomas Grenier, Carole Lartizien, Jan D'hooge, Lasse Lovstakken, Olivier Bernard
Date: 2019-08-16 00:00:00
Tasks: Decoder

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e segmenting cardiac structures as well as estimating clinical indices, on a dataset especially designed to answer this objective. We therefore introduce the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6 %. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.
Fisher Vectors Meet Neural Networks: A Hybrid Classification Architecture
Authors: Florent Perronnin, Diane Larlus
Date: 2015-08-15 00:00:00
Proceeding: Conference 2015 8
Tasks: Classification, General Classification, image-classification, Image Classification

Fisher Vectors (FV) and Convolutional Neural Networks(CNN) are two image classification pipelines with different strengths. While CNNs have shown superior accuracy on a number of classification tasks, FV classifiers are typically
less costly to train and evaluate. We propose a hybrid architecture that combines their strengths: the first unsupervised
layers rely on the FV while the subsequent fully-connected supervised layers are trained with back-propagation. We
show experimentally that this hybrid architecture significantly outperforms standard FV systems without incurring
the high cost that comes with CNNs. We also derive competitive mid-level features from our architecture that are readily applicable to other class sets and even to new tasks.
GesturePod: Enabling On-device Gesture-based Interaction for White Cane Users
Authors: Shishir G. Patil, Don Dennis, Chirag Pabbaraju, Nadeem Shaheer, Harsha Vardhan Simhadri, Vivek Seshadri, Manik Varma, Prateek Jain
Date: 2019-10-20 00:00:00
Proceeding: Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology (UIST'19) 2019 10
Tasks: Gesture Recognition, Time Series Analysis, Time Series Classification

People using white canes for navigation find it challenging to concurrently access devices such as smartphones. Building on prior research on abandonment of specialized devices, we explore a new touch free mode of interaction wherein a person with visual impairment can perform gestures on their existing white cane to trigger tasks on their smartphone. We present GesturePod, an easy-to-integrate device that clips on to any white cane, and detects gestures performed with the cane. With GesturePod, a user can perform common tasks on their smartphone without touch or even removing the phone from their pocket or bag. We discuss the challenges in building the device and our design choices. We propose a novel, efficient machine learning pipeline to train and deploy the gesture recognition model. Our in-lab study shows that GesturePod achieves 92% gesture recognition accuracy and can help perform common smartphone tasks faster. Our in-wild study suggests that GesturePod is a promising tool to improve smartphone access for people with VI, especially in constrained outdoor scenarios.
Aspect Sentiment Classification with both Word-level and Clause-level AttentionNetworks
Authors: Jingjing Wang, Jie Li, Shoushan Li, Yangyang Kang, Min Zhang, Luo Si, Guodong Zhou
Date: 2018-06-17 00:00:00
Proceeding: International Joint Conferences on Artificial Intelligence Organization 2018 6
Tasks: General Classification, Sentence, Sentiment Analysis, Sentiment Classification

Aspect sentiment classification, a challenging taskin sentiment analysis, has been attracting more andmore  attention  in  recent  years.   In  this  paper,  wehighlight the need for incorporating the importancedegrees of both words and clauses inside a sentenceand propose a hierarchical network with both word-level  and  clause-level  attentions  to  aspect  senti-ment  classification.Specifically,  we  first  adoptsentence-level discourse segmentationto  segmenta  sentence  into  several  clauses.   Then,  we  lever-age multiple Bi-directional LSTM layers to encodeall clauses and propose a word-level attention layerto capture the importance degrees of words in eachclause.  Third and finally, we leverage another Bi-directional LSTM layer to encode the output fromthe former layers and propose a clause-level atten-tion layer to capture the importance degrees of allthe  clauses  inside  a  sentence.    Experimental  re-sults  on  thelaptopandrestaurantdatasets  fromSemEval-2015 demonstrate the effectiveness of ourproposed  approach  to  aspect  sentiment  classifica-tion.
A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods
Authors: Thuy Xuan Pham, Patrick Siarry⁎, Hamouche Oulhadj
Date: 2019-05-04 00:00:00
Proceeding: journal 2019 5
Tasks: Brain Segmentation, Clustering, Image Segmentation, Medical Image Analysis, MRI segmentation, Segmentation, Semantic Segmentation

In this paper, we present a new multi-objective optimization approach for segmentation of Magnetic Resonance
Imaging (MRI) of the human brain. The proposed algorithm not only takes advantages but also solves major
drawbacks of two well-known complementary techniques, called fuzzy entropy clustering method and regionbased
active contour method, using multi-objective particle swarm optimization (MOPSO) approach. In order to
obtain accurate segmentation results, firstly, two fitness functions with independent characteristics, compactness
and separation, are derived from kernelized fuzzy entropy clustering with local spatial information and bias
correction (KFECSB) and a novel adaptive energy weight combined with global and local fitting energy active
contour (AWGLAC) model. Then, they are simultaneously optimized to finally produce a set of non-dominated
solutions, from which L2-metric method is used to select the best trade-off solution. Our algorithm is both
verified and compared with other state-of-the-art methods using simulated MR images and real MR images from
the McConnell Brain Imaging Center (BrainWeb) and the Internet Brain Segmentation Repository (IBSR), respectively.
The experimental results demonstrate that the proposed technique achieves superior segmentation
performance in terms of accuracy and robustness.
1. Introduction
Image segmentation is the process of partitioning an image space
into non-overlapped meaningful homogeneous regions or objects, according
to given quantitative criteria: gray level, color, texture or
combination of them [1]. For medical image analysis, the success of an
image analysis system depends heavily on the quality of segmentation.
We can find it in many real-life applications, for instance, in neurodegenerative
disorders such as Alzheimer's disease, in movement disorders
such as Parkinson's or Parkinson related syndrome, in congential
brain malformations or perinatal brain damage, or in post-traumatic
syndrome. However, the input MR brain images, which contain complex
structures, are inherently noisy and often corrupted by intensity
non-uniformity