October 17, 2019

3406 words 16 mins read

Paper Group ANR 834

A Fast Segmentation-free Fully Automated Approach to White Matter Injury Detection in Preterm Infants. Preference Elicitation and Robust Optimization with Multi-Attribute Quasi-Concave Choice Functions. Understanding V2V Driving Scenarios through Traffic Primitives. Generalized Bilinear Deep Convolutional Neural Networks for Multimodal Biometric Id …

A Fast Segmentation-free Fully Automated Approach to White Matter Injury Detection in Preterm Infants


Title	A Fast Segmentation-free Fully Automated Approach to White Matter Injury Detection in Preterm Infants
Authors	Subhayan Mukherjee, Irene Cheng, Steven Miller, Jessie Guo, Vann Chau, Anup Basu
Abstract	White Matter Injury (WMI) is the most prevalent brain injury in the preterm neonate leading to developmental deficits. However, detecting WMI in Magnetic Resonance (MR) images of preterm neonate brains using traditional WM segmentation-based methods is difficult mainly due to lack of reliable preterm neonate brain atlases to guide segmentation. Hence, we propose a segmentation-free, fast, unsupervised, atlas-free WMI detection method. We detect the ventricles as blobs using a fast linear Maximally Stable Extremal Regions algorithm. A reference contour equidistant from the blobs and the brain-background boundary is used to identify tissue adjacent to the blobs. Assuming normal distribution of the gray-value intensity of this tissue, the outlier intensities in the entire brain region are identified as potential WMI candidates. Thereafter, false positives are discriminated using appropriate heuristics. Experiments using an expert-annotated dataset show that the proposed method runs 20 times faster than our earlier work which relied on time-consuming segmentation of the WM region, without compromising WMI detection accuracy.
Tasks
Published	2018-07-17
URL	http://arxiv.org/abs/1807.06604v1
PDF	http://arxiv.org/pdf/1807.06604v1.pdf
PWC	https://paperswithcode.com/paper/a-fast-segmentation-free-fully-automated
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Preference Elicitation and Robust Optimization with Multi-Attribute Quasi-Concave Choice Functions


Title	Preference Elicitation and Robust Optimization with Multi-Attribute Quasi-Concave Choice Functions
Authors	William B. Haskell, Wenjie Huang, Huifu Xu
Abstract	Decision maker’s preferences are often captured by some choice functions which are used to rank prospects. In this paper, we consider ambiguity in choice functions over a multi-attribute prospect space. Our main result is a robust preference model where the optimal decision is based on the worst-case choice function from an ambiguity set constructed through preference elicitation with pairwise comparisons of prospects. Differing from existing works in the area, our focus is on quasi-concave choice functions rather than concave functions and this enables us to cover a wide range of utility/risk preference problems including multi-attribute expected utility and $S$-shaped aspirational risk preferences. The robust choice function is increasing and quasi-concave but not necessarily translation invariant, a key property of monetary risk measures. We propose two approaches based respectively on the support functions and level functions of quasi-concave functions to develop tractable formulations of the maximin preference robust optimization model. The former gives rise to a mixed integer linear programming problem whereas the latter is equivalent to solving a sequence of convex risk minimization problems. To assess the effectiveness of the proposed robust preference optimization model and numerical schemes, we apply them to a security budget allocation problem and report some preliminary results from experiments.
Tasks
Published	2018-05-17
URL	http://arxiv.org/abs/1805.06632v1
PDF	http://arxiv.org/pdf/1805.06632v1.pdf
PWC	https://paperswithcode.com/paper/preference-elicitation-and-robust
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Understanding V2V Driving Scenarios through Traffic Primitives


Title	Understanding V2V Driving Scenarios through Traffic Primitives
Authors	Wenshuo Wang, Weiyang Zhang, Ding Zhao
Abstract	Semantically understanding complex drivers’ encountering behavior, wherein two or multiple vehicles are spatially close to each other, does potentially benefit autonomous car’s decision-making design. This paper presents a framework of analyzing various encountering behaviors through decomposing driving encounter data into small building blocks, called driving primitives, using nonparametric Bayesian learning (NPBL) approaches, which offers a flexible way to gain an insight into the complex driving encounters without any prerequisite knowledge. The effectiveness of our proposed primitive-based framework is validated based on 976 naturalistic driving encounters, from which more than 4000 driving primitives are learned using NPBL - a sticky HDP-HMM, combined a hidden Markov model (HMM) with a hierarchical Dirichlet process (HDP). After that, a dynamic time warping method integrated with k-means clustering is then developed to cluster all these extracted driving primitives into groups. Experimental results find that there exist 20 kinds of driving primitives capable of representing the basic components of driving encounters in our database. This primitive-based analysis methodology potentially reveals underlying information of vehicle-vehicle encounters for self-driving applications.
Tasks	Decision Making
Published	2018-07-27
URL	http://arxiv.org/abs/1807.10422v1
PDF	http://arxiv.org/pdf/1807.10422v1.pdf
PWC	https://paperswithcode.com/paper/understanding-v2v-driving-scenarios-through
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Generalized Bilinear Deep Convolutional Neural Networks for Multimodal Biometric Identification


Title	Generalized Bilinear Deep Convolutional Neural Networks for Multimodal Biometric Identification
Authors	Sobhan Soleymani, Amirsina Torfi, Jeremy Dawson, Nasser M. Nasrabadi
Abstract	In this paper, we propose to employ a bank of modality-dedicated Convolutional Neural Networks (CNNs), fuse, train, and optimize them together for person classification tasks. A modality-dedicated CNN is used for each modality to extract modality-specific features. We demonstrate that, rather than spatial fusion at the convolutional layers, the fusion can be performed on the outputs of the fully-connected layers of the modality-specific CNNs without any loss of performance and with significant reduction in the number of parameters. We show that, using multiple CNNs with multimodal fusion at the feature-level, we significantly outperform systems that use unimodal representation. We study weighted feature, bilinear, and compact bilinear feature-level fusion algorithms for multimodal biometric person identification. Finally, We propose generalized compact bilinear fusion algorithm to deploy both the weighted feature fusion and compact bilinear schemes. We provide the results for the proposed algorithms on three challenging databases: CMU Multi-PIE, BioCop, and BIOMDATA.
Tasks	Person Identification
Published	2018-07-03
URL	http://arxiv.org/abs/1807.01298v1
PDF	http://arxiv.org/pdf/1807.01298v1.pdf
PWC	https://paperswithcode.com/paper/generalized-bilinear-deep-convolutional
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Battery health prediction under generalized conditions using a Gaussian process transition model


Title	Battery health prediction under generalized conditions using a Gaussian process transition model
Authors	Robert R. Richardson, Michael A. Osborne, David A. Howey
Abstract	Accurately predicting the future health of batteries is necessary to ensure reliable operation, minimise maintenance costs, and calculate the value of energy storage investments. The complex nature of degradation renders data-driven approaches a promising alternative to mechanistic modelling. This study predicts the changes in battery capacity over time using a Bayesian non-parametric approach based on Gaussian process regression. These changes can be integrated against an arbitrary input sequence to predict capacity fade in a variety of usage scenarios, forming a generalised health model. The approach naturally incorporates varying current, voltage and temperature inputs, crucial for enabling real world application. A key innovation is the feature selection step, where arbitrary length current, voltage and temperature measurement vectors are mapped to fixed size feature vectors, enabling them to be efficiently used as exogenous variables. The approach is demonstrated on the open-source NASA Randomised Battery Usage Dataset, with data of 26 cells aged under randomized operational conditions. Using half of the cells for training, and half for validation, the method is shown to accurately predict non-linear capacity fade, with a best case normalised root mean square error of 4.3%, including accurate estimation of prediction uncertainty.
Tasks	Feature Selection
Published	2018-07-17
URL	http://arxiv.org/abs/1807.06350v1
PDF	http://arxiv.org/pdf/1807.06350v1.pdf
PWC	https://paperswithcode.com/paper/battery-health-prediction-under-generalized
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Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification


Title	Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification
Authors	Sobhan Soleymani, Ali Dabouei, Hadi Kazemi, Jeremy Dawson, Nasser M. Nasrabadi
Abstract	In this paper, we propose a deep multimodal fusion network to fuse multiple modalities (face, iris, and fingerprint) for person identification. The proposed deep multimodal fusion algorithm consists of multiple streams of modality-specific Convolutional Neural Networks (CNNs), which are jointly optimized at multiple feature abstraction levels. Multiple features are extracted at several different convolutional layers from each modality-specific CNN for joint feature fusion, optimization, and classification. Features extracted at different convolutional layers of a modality-specific CNN represent the input at several different levels of abstract representations. We demonstrate that an efficient multimodal classification can be accomplished with a significant reduction in the number of network parameters by exploiting these multi-level abstract representations extracted from all the modality-specific CNNs. We demonstrate an increase in multimodal person identification performance by utilizing the proposed multi-level feature abstract representations in our multimodal fusion, rather than using only the features from the last layer of each modality-specific CNNs. We show that our deep multi-modal CNNs with multimodal fusion at several different feature level abstraction can significantly outperform the unimodal representation accuracy. We also demonstrate that the joint optimization of all the modality-specific CNNs excels the score and decision level fusions of independently optimized CNNs.
Tasks	Person Identification
Published	2018-07-03
URL	http://arxiv.org/abs/1807.01332v1
PDF	http://arxiv.org/pdf/1807.01332v1.pdf
PWC	https://paperswithcode.com/paper/multi-level-feature-abstraction-from
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Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Prediction


Title	Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Prediction
Authors	Long Nguyen, Jia Zhen, Zhe Lin, Hanxiang Du, Zhou Yang, Wenxuan Guo, Fang Jin
Abstract	Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop production. However, such a task is challenged by the complex interaction between crop growth and environmental and managerial factors, such as climate, soil conditions, tillage, and irrigation. In this paper, we present a novel Spatial-temporal Multi-Task Learning algorithms for within-field crop yield prediction in west Texas from 2001 to 2003. This algorithm integrates multiple heterogeneous data sources to learn different features simultaneously, and to aggregate spatial-temporal features by introducing a weighted regularizer to the loss functions. Our comprehensive experimental results consistently outperform the results of other conventional methods, and suggest a promising approach, which improves the landscape of crop prediction research fields.
Tasks	Multi-Task Learning
Published	2018-11-16
URL	http://arxiv.org/abs/1811.06665v1
PDF	http://arxiv.org/pdf/1811.06665v1.pdf
PWC	https://paperswithcode.com/paper/spatial-temporal-multi-task-learning-for
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Automated Algorithm Selection: Survey and Perspectives


Title	Automated Algorithm Selection: Survey and Perspectives
Authors	Pascal Kerschke, Holger H. Hoos, Frank Neumann, Heike Trautmann
Abstract	It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.
Tasks
Published	2018-11-28
URL	http://arxiv.org/abs/1811.11597v1
PDF	http://arxiv.org/pdf/1811.11597v1.pdf
PWC	https://paperswithcode.com/paper/automated-algorithm-selection-survey-and
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DIF : Dataset of Perceived Intoxicated Faces for Drunk Person Identification


Title	DIF : Dataset of Perceived Intoxicated Faces for Drunk Person Identification
Authors	Vineet Mehta, Devendra Pratap Yadav, Sai Srinadhu Katta, Abhinav Dhall
Abstract	Traffic accidents cause over a million deaths every year, of which a large fraction is attributed to drunk driving. An automated intoxicated driver detection system in vehicles will be useful in reducing accidents and related financial costs. Existing solutions require special equipment such as electrocardiogram, infrared cameras or breathalyzers. In this work, we propose a new dataset called DIF (Dataset of perceived Intoxicated Faces) which contains audio-visual data of intoxicated and sober people obtained from online sources. To the best of our knowledge, this is the first work for automatic bimodal non-invasive intoxication detection. Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) are trained for computing the video and audio baselines, respectively. 3D CNN is used to exploit the Spatio-temporal changes in the video. A simple variation of the traditional 3D convolution block is proposed based on inducing non-linearity between the spatial and temporal channels. Extensive experiments are performed to validate the approach and baselines.
Tasks	Person Identification
Published	2018-05-25
URL	https://arxiv.org/abs/1805.10030v3
PDF	https://arxiv.org/pdf/1805.10030v3.pdf
PWC	https://paperswithcode.com/paper/dif-dataset-of-intoxicated-faces-for-drunk
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Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features


Title	Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features
Authors	Shulong Li, Panpan Xu, Bin Li, Liyuan Chen, Zhiguo Zhou, Hongxia Hao, Yingying Duan, Michael Folkert, Jianhua Ma, Steve Jiang, Jing Wang
Abstract	To predict lung nodule malignancy with a high sensitivity and specificity, we propose a fusion algorithm that combines handcrafted features (HF) into the features learned at the output layer of a 3D deep convolutional neural network (CNN). First, we extracted twenty-nine handcrafted features, including nine intensity features, eight geometric features, and twelve texture features based on grey-level co-occurrence matrix (GLCM) averaged from thirteen directions. We then trained 3D CNNs modified from three state-of-the-art 2D CNN architectures (AlexNet, VGG-16 Net and Multi-crop Net) to extract the CNN features learned at the output layer. For each 3D CNN, the CNN features combined with the 29 handcrafted features were used as the input for the support vector machine (SVM) coupled with the sequential forward feature selection (SFS) method to select the optimal feature subset and construct the classifiers. The fusion algorithm takes full advantage of the handcrafted features and the highest level CNN features learned at the output layer. It can overcome the disadvantage of the handcrafted features that may not fully reflect the unique characteristics of a particular lesion by combining the intrinsic CNN features. Meanwhile, it also alleviates the requirement of a large scale annotated dataset for the CNNs based on the complementary of handcrafted features. The patient cohort includes 431 malignant nodules and 795 benign nodules extracted from the LIDC/IDRI database. For each investigated CNN architecture, the proposed fusion algorithm achieved the highest AUC, accuracy, sensitivity, and specificity scores among all competitive classification models.
Tasks	Feature Selection
Published	2018-09-07
URL	http://arxiv.org/abs/1809.02333v2
PDF	http://arxiv.org/pdf/1809.02333v2.pdf
PWC	https://paperswithcode.com/paper/predicting-lung-nodule-malignancies-by
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Unsupervised learning of the brain connectivity dynamic using residual D-net


Title	Unsupervised learning of the brain connectivity dynamic using residual D-net
Authors	Youngjoo Seo, Manuel Morante, Yannis Kopsinis, Sergios Theodoridis
Abstract	In this paper, we propose a novel unsupervised learning method to learn the brain dynamics using a deep learning architecture named residual D-net. As it is often the case in medical research, in contrast to typical deep learning tasks, the size of the resting-state functional Magnetic Resonance Image (rs-fMRI) datasets for training is limited. Thus, the available data should be very efficiently used to learn the complex patterns underneath the brain connectivity dynamics. To address this issue, we use residual connections to alleviate the training complexity through recurrent multi-scale representation. We conduct two classification tasks to differentiate early and late stage Mild Cognitive Impairment (MCI) from Normal healthy Control (NC) subjects. The experiments verify that our proposed residual D-net indeed learns the brain connectivity dynamics, leading to significantly higher classification accuracy compared to previously published techniques.
Tasks
Published	2018-04-20
URL	http://arxiv.org/abs/1804.07672v2
PDF	http://arxiv.org/pdf/1804.07672v2.pdf
PWC	https://paperswithcode.com/paper/unsupervised-learning-of-the-brain
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Progressive Memory Banks for Incremental Domain Adaptation


Title	Progressive Memory Banks for Incremental Domain Adaptation
Authors	Nabiha Asghar, Lili Mou, Kira A. Selby, Kevin D. Pantasdo, Pascal Poupart, Xin Jiang
Abstract	This paper addresses the problem of incremental domain adaptation (IDA) in natural language processing (NLP). We assume each domain comes one after another, and that we could only access data in the current domain. The goal of IDA is to build a unified model performing well on all the domains that we have encountered. We adopt the recurrent neural network (RNN) widely used in NLP, but augment it with a directly parameterized memory bank, which is retrieved by an attention mechanism at each step of RNN transition. The memory bank provides a natural way of IDA: when adapting our model to a new domain, we progressively add new slots to the memory bank, which increases the number of parameters, and thus the model capacity. We learn the new memory slots and fine-tune existing parameters by back-propagation. Experimental results show that our approach achieves significantly better performance than fine-tuning alone. Compared with expanding hidden states, our approach is more robust for old domains, shown by both empirical and theoretical results. Our model also outperforms previous work of IDA including elastic weight consolidation and progressive neural networks in the experiments.
Tasks	Domain Adaptation
Published	2018-11-01
URL	https://arxiv.org/abs/1811.00239v2
PDF	https://arxiv.org/pdf/1811.00239v2.pdf
PWC	https://paperswithcode.com/paper/progressive-memory-banks-for-incremental
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Inference, Learning, and Population Size: Projectivity for SRL Models


Title	Inference, Learning, and Population Size: Projectivity for SRL Models
Authors	Manfred Jaeger, Oliver Schulte
Abstract	A subtle difference between propositional and relational data is that in many relational models, marginal probabilities depend on the population or domain size. This paper connects the dependence on population size to the classic notion of projectivity from statistical theory: Projectivity implies that relational predictions are robust with respect to changes in domain size. We discuss projectivity for a number of common SRL systems, and identify syntactic fragments that are guaranteed to yield projective models. The syntactic conditions are restrictive, which suggests that projectivity is difficult to achieve in SRL, and care must be taken when working with different domain sizes.
Tasks
Published	2018-07-02
URL	http://arxiv.org/abs/1807.00564v1
PDF	http://arxiv.org/pdf/1807.00564v1.pdf
PWC	https://paperswithcode.com/paper/inference-learning-and-population-size
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Data-Augmented Contact Model for Rigid Body Simulation


Title	Data-Augmented Contact Model for Rigid Body Simulation
Authors	Yifeng Jiang, Jiazheng Sun, C. Karen Liu
Abstract	Accurately modeling contact behaviors for real-world, near-rigid materials remains a grand challenge for existing rigid-body physics simulators. This paper introduces a data-augmented contact model that incorporates analytical solutions with observed data to predict the 3D contact impulse which could result in rigid bodies bouncing, sliding or spinning in all directions. Our method enhances the expressiveness of the standard Coulomb contact model by learning the contact behaviors from the observed data, while preserving the fundamental contact constraints whenever possible. For example, a classifier is trained to approximate the transitions between static and dynamic frictions, while non-penetration constraint during collision is enforced analytically. Our method computes the aggregated effect of contact for the entire rigid body, instead of predicting the contact force for each contact point individually, removing the exponential decline in accuracy as the number of contact points increases.
Tasks
Published	2018-03-11
URL	http://arxiv.org/abs/1803.04019v3
PDF	http://arxiv.org/pdf/1803.04019v3.pdf
PWC	https://paperswithcode.com/paper/data-augmented-contact-model-for-rigid-body
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Learning-Based Quality Control for Cardiac MR Images


Title	Learning-Based Quality Control for Cardiac MR Images
Authors	Giacomo Tarroni, Ozan Oktay, Wenjia Bai, Andreas Schuh, Hideaki Suzuki, Jonathan Passerat-Palmbach, Antonio de Marvao, Declan P. O’Regan, Stuart Cook, Ben Glocker, Paul M. Matthews, Daniel Rueckert
Abstract	The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artefacts such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images: however, this procedure is strongly operator-dependent, cumbersome and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully-automated, learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation, 2) inter-slice motion detection, 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method - integrating both regression and structured classification models - to extract landmarks as well as probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank as well as on 100 cases from the UK Digital Heart Project, and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition.
Tasks	Motion Detection
Published	2018-03-25
URL	http://arxiv.org/abs/1803.09354v2
PDF	http://arxiv.org/pdf/1803.09354v2.pdf
PWC	https://paperswithcode.com/paper/learning-based-quality-control-for-cardiac-mr
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