April 2, 2020

3124 words 15 mins read

Paper Group ANR 274

Enriching Consumer Health Vocabulary Using Enhanced GloVe Word Embedding. Learning Generative Models of Tissue Organization with Supervised GANs. Influence of Initialization on the Performance of Metaheuristic Optimizers. Widening and Squeezing: Towards Accurate and Efficient QNNs. AM-MobileNet1D: A Portable Model for Speaker Recognition. A CNN Wit …

Enriching Consumer Health Vocabulary Using Enhanced GloVe Word Embedding


Title	Enriching Consumer Health Vocabulary Using Enhanced GloVe Word Embedding
Authors	Mohammed Ibrahim, Susan Gauch, Omar Salman, Mohammed Alqahatani
Abstract	Open-Access and Collaborative Consumer Health Vocabulary (OAC CHV, or CHV for short), is a collection of medical terms written in plain English. It provides a list of simple, easy, and clear terms that laymen prefer to use rather than an equivalent professional medical term. The National Library of Medicine (NLM) has integrated and mapped the CHV terms to their Unified Medical Language System (UMLS). These CHV terms mapped to 56000 professional concepts on the UMLS. We found that about 48% of these laymen’s terms are still jargon and matched with the professional terms on the UMLS. In this paper, we present an enhanced word embedding technique that generates new CHV terms from a consumer-generated text. We downloaded our corpus from a healthcare social media and evaluated our new method based on iterative feedback to word embedding using ground truth built from the existing CHV terms. Our feedback algorithm outperformed unmodified GLoVe and new CHV terms have been detected.
Tasks
Published	2020-03-31
URL	https://arxiv.org/abs/2004.00150v1
PDF	https://arxiv.org/pdf/2004.00150v1.pdf
PWC	https://paperswithcode.com/paper/enriching-consumer-health-vocabulary-using
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Learning Generative Models of Tissue Organization with Supervised GANs


Title	Learning Generative Models of Tissue Organization with Supervised GANs
Authors	Ligong Han, Robert F. Murphy, Deva Ramanan
Abstract	A key step in understanding the spatial organization of cells and tissues is the ability to construct generative models that accurately reflect that organization. In this paper, we focus on building generative models of electron microscope (EM) images in which the positions of cell membranes and mitochondria have been densely annotated, and propose a two-stage procedure that produces realistic images using Generative Adversarial Networks (or GANs) in a supervised way. In the first stage, we synthesize a label “image” given a noise “image” as input, which then provides supervision for EM image synthesis in the second stage. The full model naturally generates label-image pairs. We show that accurate synthetic EM images are produced using assessment via (1) shape features and global statistics, (2) segmentation accuracies, and (3) user studies. We also demonstrate further improvements by enforcing a reconstruction loss on intermediate synthetic labels and thus unifying the two stages into one single end-to-end framework.
Tasks	Image Generation
Published	2020-03-31
URL	https://arxiv.org/abs/2004.00140v1
PDF	https://arxiv.org/pdf/2004.00140v1.pdf
PWC	https://paperswithcode.com/paper/learning-generative-models-of-tissue
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Influence of Initialization on the Performance of Metaheuristic Optimizers


Title	Influence of Initialization on the Performance of Metaheuristic Optimizers
Authors	Qian Li, San-Yang Liu, Xin-She Yang
Abstract	All metaheuristic optimization algorithms require some initialization, and the initialization for such optimizers is usually carried out randomly. However, initialization can have some significant influence on the performance of such algorithms. This paper presents a systematic comparison of 22 different initialization methods on the convergence and accuracy of five optimizers: differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), artificial bee colony (ABC) algorithm and genetic algorithm (GA). We have used 19 different test functions with different properties and modalities to compare the possible effects of initialization, population sizes and the numbers of iterations. Rigorous statistical ranking tests indicate that 43.37% of the functions using the DE algorithm show significant differences for different initialization methods, while 73.68% of the functions using both PSO and CS algorithms are significantly affected by different initialization methods. The simulations show that DE is less sensitive to initialization, while both PSO and CS are more sensitive to initialization. In addition, under the condition of the same maximum number of function evaluations (FEs), the population size can also have a strong effect. Particle swarm optimization usually requires a larger population, while the cuckoo search needs only a small population size. Differential evolution depends more heavily on the number of iterations, a relatively small population with more iterations can lead to better results. Furthermore, ABC is more sensitive to initialization, while such initialization has little effect on GA. Some probability distributions such as the beta distribution, exponential distribution and Rayleigh distribution can usually lead to better performance. The implications of this study and further research topics are also discussed in detail.
Tasks
Published	2020-03-08
URL	https://arxiv.org/abs/2003.03789v1
PDF	https://arxiv.org/pdf/2003.03789v1.pdf
PWC	https://paperswithcode.com/paper/influence-of-initialization-on-the
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Widening and Squeezing: Towards Accurate and Efficient QNNs


Title	Widening and Squeezing: Towards Accurate and Efficient QNNs
Authors	Chuanjian Liu, Kai Han, Yunhe Wang, Hanting Chen, Qi Tian, Chunjing Xu
Abstract	Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than that of networks with full-precision parameters. Most of existing methods aim to enhance performance of QNNs especially binary neural networks by exploiting more effective training techniques. However, we find the representation capability of quantization features is far weaker than full-precision features by experiments. We address this problem by projecting features in original full-precision networks to high-dimensional quantization features. Simultaneously, redundant quantization features will be eliminated in order to avoid unrestricted growth of dimensions for some datasets. Then, a compact quantization neural network but with sufficient representation ability will be established. Experimental results on benchmark datasets demonstrate that the proposed method is able to establish QNNs with much less parameters and calculations but almost the same performance as that of full-precision baseline models, e.g. $29.9%$ top-1 error of binary ResNet-18 on the ImageNet ILSVRC 2012 dataset.
Tasks	Quantization
Published	2020-02-03
URL	https://arxiv.org/abs/2002.00555v2
PDF	https://arxiv.org/pdf/2002.00555v2.pdf
PWC	https://paperswithcode.com/paper/widening-and-squeezing-towards-accurate-and
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AM-MobileNet1D: A Portable Model for Speaker Recognition


Title	AM-MobileNet1D: A Portable Model for Speaker Recognition
Authors	João Antônio Chagas Nunes, David Macêdo, Cleber Zanchettin
Abstract	Speaker Recognition and Speaker Identification are challenging tasks with essential applications such as automation, authentication, and security. Deep learning approaches like SincNet and AM-SincNet presented great results on these tasks. The promising performance took these models to real-world applications that becoming fundamentally end-user driven and mostly mobile. The mobile computation requires applications with reduced storage size, non-processing and memory intensive and efficient energy-consuming. The deep learning approaches, in contrast, usually are energy expensive, demanding storage, processing power, and memory. To address this demand, we propose a portable model called Additive Margin MobileNet1D (AM-MobileNet1D) to Speaker Identification on mobile devices. We evaluated the proposed approach on TIMIT and MIT datasets obtaining equivalent or better performances concerning the baseline methods. Additionally, the proposed model takes only 11.6 megabytes on disk storage against 91.2 from SincNet and AM-SincNet architectures, making the model seven times faster, with eight times fewer parameters.
Tasks	Speaker Identification, Speaker Recognition
Published	2020-03-31
URL	https://arxiv.org/abs/2004.00132v1
PDF	https://arxiv.org/pdf/2004.00132v1.pdf
PWC	https://paperswithcode.com/paper/am-mobilenet1d-a-portable-model-for-speaker
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A CNN With Multi-scale Convolution for Hyperspectral Image Classification using Target-Pixel-Orientation scheme


Title	A CNN With Multi-scale Convolution for Hyperspectral Image Classification using Target-Pixel-Orientation scheme
Authors	Jayasree Saha, Yuvraj Khanna, Jayanta Mukherjee
Abstract	Recently, CNN is a popular choice to handle the hyperspectral image classification challenges. In spite of having such large spectral information in Hyper-Spectral Image(s) (HSI), it creates a curse of dimensionality. Also, large spatial variability of spectral signature adds more difficulty in classification problem. Additionally, training a CNN in the end to end fashion with scarced training examples is another challenging and interesting problem. In this paper, a novel target-patch-orientation method is proposed to train a CNN based network. Also, we have introduced a hybrid of 3D-CNN and 2D-CNN based network architecture to implement band reduction and feature extraction methods, respectively. Experimental results show that our method outperforms the accuracies reported in the existing state of the art methods.
Tasks	Hyperspectral Image Classification, Image Classification
Published	2020-01-30
URL	https://arxiv.org/abs/2001.11198v2
PDF	https://arxiv.org/pdf/2001.11198v2.pdf
PWC	https://paperswithcode.com/paper/a-cnn-with-multi-scale-convolution-for
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Frequency-based Search-control in Dyna


Title	Frequency-based Search-control in Dyna
Authors	Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand
Abstract	Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency. In particular, Dyna is an elegant model-based architecture integrating learning and planning that provides huge flexibility of using a model. One of the most important components in Dyna is called search-control, which refers to the process of generating state or state-action pairs from which we query the model to acquire simulated experiences. Search-control is critical in improving learning efficiency. In this work, we propose a simple and novel search-control strategy by searching high frequency regions of the value function. Our main intuition is built on Shannon sampling theorem from signal processing, which indicates that a high frequency signal requires more samples to reconstruct. We empirically show that a high frequency function is more difficult to approximate. This suggests a search-control strategy: we should use states from high frequency regions of the value function to query the model to acquire more samples. We develop a simple strategy to locally measure the frequency of a function by gradient and hessian norms, and provide theoretical justification for this approach. We then apply our strategy to search-control in Dyna, and conduct experiments to show its property and effectiveness on benchmark domains.
Tasks
Published	2020-02-14
URL	https://arxiv.org/abs/2002.05822v1
PDF	https://arxiv.org/pdf/2002.05822v1.pdf
PWC	https://paperswithcode.com/paper/frequency-based-search-control-in-dyna-1
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APPLD: Adaptive Planner Parameter Learning from Demonstration


Title	APPLD: Adaptive Planner Parameter Learning from Demonstration
Authors	Xuesu Xiao, Bo Liu, Garrett Warnell, Jonathan Fink, Peter Stone
Abstract	Existing autonomous robot navigation systems allow robots to move from one point to another in a collision-free manner. However, when facing new environments, these systems generally require re-tuning by expert roboticists with a good understanding of the inner workings of the navigation system. In contrast, even users who are unversed in the details of robot navigation algorithms can generate desirable navigation behavior in new environments via teleoperation. In this paper, we introduce APPLD, Adaptive Planner Parameter Learning from Demonstration, that allows existing navigation systems to be successfully applied to new complex environments, given only a human teleoperated demonstration of desirable navigation. APPLD is verified on two robots running different navigation systems in different environments. Experimental results show that APPLD can outperform navigation systems with the default and expert-tuned parameters, and even the human demonstrator themselves.
Tasks	Robot Navigation
Published	2020-03-31
URL	https://arxiv.org/abs/2004.00116v1
PDF	https://arxiv.org/pdf/2004.00116v1.pdf
PWC	https://paperswithcode.com/paper/appld-adaptive-planner-parameter-learning
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Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images


Title	Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images
Authors	Sharib Ali, Binod Bhattarai, Tae-Kyun Kim, Jens Rittscher
Abstract	Endoscopy is a widely used imaging modality to diagnose and treat diseases in hollow organs as for example the gastrointestinal tract, the kidney and the liver. However, due to varied modalities and use of different imaging protocols at various clinical centers impose significant challenges when generalising deep learning models. Moreover, the assembly of large datasets from different clinical centers can introduce a huge label bias that renders any learnt model unusable. Also, when using new modality or presence of images with rare patterns, a bulk amount of similar image data and their corresponding labels are required for training these models. In this work, we propose to use a few-shot learning approach that requires less training data and can be used to predict label classes of test samples from an unseen dataset. We propose a novel additive angular margin metric in the framework of prototypical network in few-shot learning setting. We compare our approach to the several established methods on a large cohort of multi-center, multi-organ, and multi-modal endoscopy data. The proposed algorithm outperforms existing state-of-the-art methods.
Tasks	Few-Shot Learning
Published	2020-03-23
URL	https://arxiv.org/abs/2003.10033v2
PDF	https://arxiv.org/pdf/2003.10033v2.pdf
PWC	https://paperswithcode.com/paper/additive-angular-margin-for-few-shot-learning
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Incorporating Symmetry into Deep Dynamics Models for Improved Generalization


Title	Incorporating Symmetry into Deep Dynamics Models for Improved Generalization
Authors	Rui Wang, Robin Walters, Rose Yu
Abstract	Training machine learning models that can learn complex spatiotemporal dynamics and generalize under distributional shift is a fundamental challenge. The symmetries in a physical system play a unique role in characterizing unchanged features under transformation. We propose a systematic approach to improve generalization in spatiotemporal models by incorporating symmetries into deep neural networks. Our general framework to design equivariant convolutional models employs (1) convolution with equivariant kernels, (2) conjugation by averaging operators in order to force equivariance, (3) and a naturally equivariant generalization of convolution called group correlation. Our framework is both theoretically and experimentally robust to distributional shift by a symmetry group and enjoys favorable sample complexity. We demonstrate the advantage of our approach on a variety of physical dynamics including turbulence and diffusion systems. This is the first time that equivariant CNNs have been used to forecast physical dynamics.
Tasks
Published	2020-02-08
URL	https://arxiv.org/abs/2002.03061v2
PDF	https://arxiv.org/pdf/2002.03061v2.pdf
PWC	https://paperswithcode.com/paper/incorporating-symmetry-into-deep-dynamics
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Framework

Extremal Region Analysis based Deep Learning Framework for Detecting Defects


Title	Extremal Region Analysis based Deep Learning Framework for Detecting Defects
Authors	Zelin Deng, Xiaolong Yan, Shengjun Zhang, Colleen P. Bailey
Abstract	A maximally stable extreme region (MSER) analysis based convolutional neural network (CNN) for unified defect detection framework is proposed in this paper. Our proposed framework utilizes the generality and stability of MSER to generate the desired defect candidates. Then a specific trained binary CNN classifier is adopted over the defect candidates to produce the final defect set. Defect datasets over different categories \blue{are used} in the experiments. More generally, the parameter settings in MSER can be adjusted to satisfy different requirements in various industries (high precision, high recall, etc). Extensive experimental results have shown the efficacy of the proposed framework.
Tasks
Published	2020-03-19
URL	https://arxiv.org/abs/2003.08525v1
PDF	https://arxiv.org/pdf/2003.08525v1.pdf
PWC	https://paperswithcode.com/paper/extremal-region-analysis-based-deep-learning
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ResDepth: Learned Residual Stereo Reconstruction


Title	ResDepth: Learned Residual Stereo Reconstruction
Authors	Corinne Stucker, Konrad Schindler
Abstract	We propose an embarrassingly simple, but very effective scheme for high-quality dense stereo reconstruction: (i) generate an approximate reconstruction with your favourite stereo matcher; (ii) rewarp the input images with that approximate model; and (iii) with the initial reconstruction and the warped images as input, train a deep network to enhance the reconstruction by regressing a residual correction. The strategy to only learn the residual greatly simplifies the learning problem. A standard Unet without bells and whistles is enough to reconstruct even small surface details, like dormers and roof substructures in satellite images. We also investigate residual reconstruction with less information and find that even a single image is enough to greatly improve an approximate reconstruction. Our full model reduces the mean absolute error of state-of-the-art stereo reconstruction systems by >50%, both in our target domain of satellite stereo and on stereo pairs from the ETH3D benchmark.
Tasks
Published	2020-01-22
URL	https://arxiv.org/abs/2001.08026v1
PDF	https://arxiv.org/pdf/2001.08026v1.pdf
PWC	https://paperswithcode.com/paper/resdepth-learned-residual-stereo
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Framework

PuzzleNet: Scene Text Detection by Segment Context Graph Learning


Title	PuzzleNet: Scene Text Detection by Segment Context Graph Learning
Authors	Hao Liu, Antai Guo, Deqiang Jiang, Yiqing Hu, Bo Ren
Abstract	Recently, a series of decomposition-based scene text detection methods has achieved impressive progress by decomposing challenging text regions into pieces and linking them in a bottom-up manner. However, most of them merely focus on linking independent text pieces while the context information is underestimated. In the puzzle game, the solver often put pieces together in a logical way according to the contextual information of each piece, in order to arrive at the correct solution. Inspired by it, we propose a novel decomposition-based method, termed Puzzle Networks (PuzzleNet), to address the challenging scene text detection task in this work. PuzzleNet consists of the Segment Proposal Network (SPN) that predicts the candidate text segments fitting arbitrary shape of text region, and the two-branch Multiple-Similarity Graph Convolutional Network (MSGCN) that models both appearance and geometry correlations between each segment to its contextual ones. By building segments as context graphs, MSGCN effectively employs segment context to predict combinations of segments. Final detections of polygon shape are produced by merging segments according to the predicted combinations. Evaluations on three benchmark datasets, ICDAR15, MSRA-TD500 and SCUT-CTW1500, have demonstrated that our method can achieve better or comparable performance than current state-of-the-arts, which is beneficial from the exploitation of segment context graph.
Tasks	Scene Text Detection
Published	2020-02-26
URL	https://arxiv.org/abs/2002.11371v1
PDF	https://arxiv.org/pdf/2002.11371v1.pdf
PWC	https://paperswithcode.com/paper/puzzlenet-scene-text-detection-by-segment
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Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR


Title	Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR
Authors	Rebecca Bendayan, Honghan Wu, Zeljko Kraljevic, Robert Stewart, Tom Searle, Jaya Chaturvedi, Jayati Das-Munshi, Zina Ibrahim, Aurelie Mascio, Angus Roberts, Daniel Bean, Richard Dobson
Abstract	Multimorbidity research in mental health services requires data from physical health conditions which is traditionally limited in mental health care electronic health records. In this study, we aimed to extract data from physical health conditions from clinical notes using SemEHR. Data was extracted from Clinical Record Interactive Search (CRIS) system at South London and Maudsley Biomedical Research Centre (SLaM BRC) and the cohort consisted of all individuals who had received a primary or secondary diagnosis of severe mental illness between 2007 and 2018. Three pairs of annotators annotated 2403 documents with an average Cohen’s Kappa of 0.757. Results show that the NLP performance varies across different diseases areas (F1 0.601 - 0.954) suggesting that the language patterns or terminologies of different condition groups entail different technical challenges to the same NLP task.
Tasks
Published	2020-02-07
URL	https://arxiv.org/abs/2002.08901v1
PDF	https://arxiv.org/pdf/2002.08901v1.pdf
PWC	https://paperswithcode.com/paper/identifying-physical-health-comorbidities-in
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Synergizing Domain Expertise with Self-Awareness in Software Systems: A Patternized Architecture Guideline


Title	Synergizing Domain Expertise with Self-Awareness in Software Systems: A Patternized Architecture Guideline
Authors	Tao Chen, Rami Bahsoon, Xin Yao
Abstract	To promote engineering self-aware and self-adaptive software systems in a reusable manner, architectural patterns and the related methodology provide an unified solution to handle the recurring problems in the engineering process. However, in existing patterns and methods, domain knowledge and engineers’ expertise that is built over time are not explicitly linked to the self-aware processes. This linkage is important, as the knowledge is a valuable asset for the related problems and its absence would cause unnecessary overhead, possibly misleading results and unwise waste of the tremendous benefit that could have been brought by the domain expertise. This paper highlights the importance of synergizing domain expertise and the self-awareness to enable better self-adaptation in software systems, relying on well-defined expertise representation, algorithms and techniques. In particular, we present a holistic framework of notions, enriched patterns and methodology, dubbed DBASES, that offers a principled guideline for the engineers to perform difficulty and benefit analysis on possible synergies, in an attempt to keep “engineers-in-the-loop”. Through three tutorial case studies, we demonstrate how DBASES can be applied in different domains, within which a carefully selected set of candidates with different synergies can be used for quantitative investigation, providing more informed decisions of the design choices.
Tasks
Published	2020-01-20
URL	https://arxiv.org/abs/2001.07076v2
PDF	https://arxiv.org/pdf/2001.07076v2.pdf
PWC	https://paperswithcode.com/paper/synergizing-domain-expertise-with-self
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