July 28, 2019

2796 words 14 mins read

Paper Group ANR 182

Improving Gravitational Search Algorithm Performance with Artificial Bee Colony Algorithm for Constrained Numerical Optimization. Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation. Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning. Mining Functional Modules by Multiview-NMF of Phenome-Genome Associat …

Improving Gravitational Search Algorithm Performance with Artificial Bee Colony Algorithm for Constrained Numerical Optimization


Title	Improving Gravitational Search Algorithm Performance with Artificial Bee Colony Algorithm for Constrained Numerical Optimization
Authors	Hasan Ali Akyürek, Ömer Kaan Baykan, Barış Koçer
Abstract	In this paper, we propose an improved gravitational search algorithm named GSABC. The algorithm improves gravitational search algorithm (GSA) results improved by using artificial bee colony algorithm (ABC) to solve constrained numerical optimization problems. In GSA, solutions are attracted towards each other by applying gravitational forces, which depending on the masses assigned to the solutions, to each other. The heaviest mass will move slower than other masses and gravitate others. Due to nature of gravitation, GSA may pass global minimum if some solutions stuck to local minimum. ABC updates the positions of the best solutions that has obtained from GSA, preventing the GSA from sticking to the local minimum by its strong searching ability. The proposed algorithm improves the performance of GSA. The proposed method tested on 23 well-known unimodal, multimodal and fixed-point multimodal benchmark test functions. Experimental results show that GSABC outperforms or performs similarly to five state-of-the-art optimization approaches.
Tasks
Published	2017-01-16
URL	http://arxiv.org/abs/1706.03608v1
PDF	http://arxiv.org/pdf/1706.03608v1.pdf
PWC	https://paperswithcode.com/paper/improving-gravitational-search-algorithm
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Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation


Title	Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation
Authors	Manu Goyal, Neil D. Reeves, Satyan Rajbhandari, Jennifer Spragg, Moi Hoon Yap
Abstract	Diabetic Foot Ulcer (DFU) is a major complication of Diabetes, which if not managed properly can lead to amputation. DFU can appear anywhere on the foot and can vary in size, colour, and contrast depending on various pathologies. Current clinical approaches to DFU treatment rely on patients and clinician vigilance, which has significant limitations such as the high cost involved in the diagnosis, treatment and lengthy care of the DFU. We introduce a dataset of 705 foot images. We provide the ground truth of ulcer region and the surrounding skin that is an important indicator for clinicians to assess the progress of ulcer. Then, we propose a two-tier transfer learning from bigger datasets to train the Fully Convolutional Networks (FCNs) to automatically segment the ulcer and surrounding skin. Using 5-fold cross-validation, the proposed two-tier transfer learning FCN Models achieve a Dice Similarity Coefficient of 0.794 ($\pm$0.104) for ulcer region, 0.851 ($\pm$0.148) for surrounding skin region, and 0.899 ($\pm$0.072) for the combination of both regions. This demonstrates the potential of FCNs in DFU segmentation, which can be further improved with a larger dataset.
Tasks	Transfer Learning
Published	2017-08-06
URL	http://arxiv.org/abs/1708.01928v1
PDF	http://arxiv.org/pdf/1708.01928v1.pdf
PWC	https://paperswithcode.com/paper/fully-convolutional-networks-for-diabetic
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Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning


Title	Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning
Authors	Yang Hu, Yun Gu, Jie Yang, Guang-Zhong Yang
Abstract	In robotic surgery, task automation and learning from demonstration combined with human supervision is an emerging trend for many new surgical robot platforms. One such task is automated anastomosis, which requires bimanual needle handling and suture detection. Due to the complexity of the surgical environment and varying patient anatomies, reliable suture detection is difficult, which is further complicated by occlusion and thread topologies. In this paper, we propose a multi-stage framework for suture thread detection based on deep learning. Fully convolutional neural networks are used to obtain the initial detection and the overlapping status of suture thread, which are later fused with the original image to learn a gradient road map of the thread. Based on the gradient road map, multiple segments of the thread are extracted and linked to form the whole thread using a curvilinear structure detector. Experiments on two different types of sutures demonstrate the accuracy of the proposed framework.
Tasks
Published	2017-11-08
URL	http://arxiv.org/abs/1711.03179v1
PDF	http://arxiv.org/pdf/1711.03179v1.pdf
PWC	https://paperswithcode.com/paper/multi-stage-suture-detection-for-robot
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Mining Functional Modules by Multiview-NMF of Phenome-Genome Association


Title	Mining Functional Modules by Multiview-NMF of Phenome-Genome Association
Authors	YaoGong Zhang, YingJie Xu, Xin Fan, YuXiang Hong, Jiahui Liu, ZhiCheng He, YaLou Huang, MaoQiang Xie
Abstract	Background: Mining gene modules from genomic data is an important step to detect gene members of pathways or other relations such as protein-protein interactions. In this work, we explore the plausibility of detecting gene modules by factorizing gene-phenotype associations from a phenotype ontology rather than the conventionally used gene expression data. In particular, the hierarchical structure of ontology has not been sufficiently utilized in clustering genes while functionally related genes are consistently associated with phenotypes on the same path in the phenotype ontology. Results: We propose a hierarchal Nonnegative Matrix Factorization (NMF)-based method, called Consistent Multiple Nonnegative Matrix Factorization (CMNMF), to factorize genome-phenome association matrix at two levels of the hierarchical structure in phenotype ontology for mining gene functional modules. CMNMF constrains the gene clusters from the association matrices at two consecutive levels to be consistent since the genes are annotated with both the child phenotype and the parent phenotype in the consecutive levels. CMNMF also restricts the identified phenotype clusters to be densely connected in the phenotype ontology hierarchy. In the experiments on mining functionally related genes from mouse phenotype ontology and human phenotype ontology, CMNMF effectively improved clustering performance over the baseline methods. Gene ontology enrichment analysis was also conducted to reveal interesting gene modules. Conclusions: Utilizing the information in the hierarchical structure of phenotype ontology, CMNMF can identify functional gene modules with more biological significance than the conventional methods. CMNMF could also be a better tool for predicting members of gene pathways and protein-protein interactions. Availability: https://github.com/nkiip/CMNMF
Tasks
Published	2017-05-11
URL	http://arxiv.org/abs/1705.03998v1
PDF	http://arxiv.org/pdf/1705.03998v1.pdf
PWC	https://paperswithcode.com/paper/mining-functional-modules-by-multiview-nmf-of
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Neural Domain Adaptation for Biomedical Question Answering


Title	Neural Domain Adaptation for Biomedical Question Answering
Authors	Georg Wiese, Dirk Weissenborn, Mariana Neves
Abstract	Factoid question answering (QA) has recently benefited from the development of deep learning (DL) systems. Neural network models outperform traditional approaches in domains where large datasets exist, such as SQuAD (ca. 100,000 questions) for Wikipedia articles. However, these systems have not yet been applied to QA in more specific domains, such as biomedicine, because datasets are generally too small to train a DL system from scratch. For example, the BioASQ dataset for biomedical QA comprises less then 900 factoid (single answer) and list (multiple answers) QA instances. In this work, we adapt a neural QA system trained on a large open-domain dataset (SQuAD, source) to a biomedical dataset (BioASQ, target) by employing various transfer learning techniques. Our network architecture is based on a state-of-the-art QA system, extended with biomedical word embeddings and a novel mechanism to answer list questions. In contrast to existing biomedical QA systems, our system does not rely on domain-specific ontologies, parsers or entity taggers, which are expensive to create. Despite this fact, our systems achieve state-of-the-art results on factoid questions and competitive results on list questions.
Tasks	Domain Adaptation, Question Answering, Transfer Learning, Word Embeddings
Published	2017-06-12
URL	http://arxiv.org/abs/1706.03610v2
PDF	http://arxiv.org/pdf/1706.03610v2.pdf
PWC	https://paperswithcode.com/paper/neural-domain-adaptation-for-biomedical
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An ADMM Approach to Masked Signal Decomposition Using Subspace Representation


Title	An ADMM Approach to Masked Signal Decomposition Using Subspace Representation
Authors	Shervin Minaee, Yao Wang
Abstract	Signal decomposition is a classical problem in signal processing, which aims to separate an observed signal into two or more components each with its own property. Usually each component is described by its own subspace or dictionary. Extensive research has been done for the case where the components are additive, but in real world applications, the components are often non-additive. For example, an image may consist of a foreground object overlaid on a background, where each pixel either belongs to the foreground or the background. In such a situation, to separate signal components, we need to find a binary mask which shows the location of each component. Therefore it requires to solve a binary optimization problem. Since most of the binary optimization problems are intractable, we relax this problem to the approximated continuous problem, and solve it by alternating optimization technique. We show the application of the proposed algorithm for three applications: separation of text from background in images, separation of moving objects from a background undergoing global camera motion in videos, separation of sinusoidal and spike components in one dimensional signals. We demonstrate in each case that considering the non-additive nature of the problem can lead to significant improvement.
Tasks
Published	2017-04-25
URL	http://arxiv.org/abs/1704.07711v2
PDF	http://arxiv.org/pdf/1704.07711v2.pdf
PWC	https://paperswithcode.com/paper/an-admm-approach-to-masked-signal
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Single image depth estimation by dilated deep residual convolutional neural network and soft-weight-sum inference


Title	Single image depth estimation by dilated deep residual convolutional neural network and soft-weight-sum inference
Authors	Bo Li, Yuchao Dai, Huahui Chen, Mingyi He
Abstract	This paper proposes a new residual convolutional neural network (CNN) architecture for single image depth estimation. Compared with existing deep CNN based methods, our method achieves much better results with fewer training examples and model parameters. The advantages of our method come from the usage of dilated convolution, skip connection architecture and soft-weight-sum inference. Experimental evaluation on the NYU Depth V2 dataset shows that our method outperforms other state-of-the-art methods by a margin.
Tasks	Depth Estimation
Published	2017-04-27
URL	http://arxiv.org/abs/1705.00534v1
PDF	http://arxiv.org/pdf/1705.00534v1.pdf
PWC	https://paperswithcode.com/paper/single-image-depth-estimation-by-dilated-deep
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Deep View Morphing


Title	Deep View Morphing
Authors	Dinghuang Ji, Junghyun Kwon, Max McFarland, Silvio Savarese
Abstract	Recently, convolutional neural networks (CNN) have been successfully applied to view synthesis problems. However, such CNN-based methods can suffer from lack of texture details, shape distortions, or high computational complexity. In this paper, we propose a novel CNN architecture for view synthesis called “Deep View Morphing” that does not suffer from these issues. To synthesize a middle view of two input images, a rectification network first rectifies the two input images. An encoder-decoder network then generates dense correspondences between the rectified images and blending masks to predict the visibility of pixels of the rectified images in the middle view. A view morphing network finally synthesizes the middle view using the dense correspondences and blending masks. We experimentally show the proposed method significantly outperforms the state-of-the-art CNN-based view synthesis method.
Tasks
Published	2017-03-07
URL	http://arxiv.org/abs/1703.02168v1
PDF	http://arxiv.org/pdf/1703.02168v1.pdf
PWC	https://paperswithcode.com/paper/deep-view-morphing
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Dynamic Oracle for Neural Machine Translation in Decoding Phase


Title	Dynamic Oracle for Neural Machine Translation in Decoding Phase
Authors	Zi-Yi Dou, Hao Zhou, Shu-Jian Huang, Xin-Yu Dai, Jia-Jun Chen
Abstract	The past several years have witnessed the rapid progress of end-to-end Neural Machine Translation (NMT). However, there exists discrepancy between training and inference in NMT when decoding, which may lead to serious problems since the model might be in a part of the state space it has never seen during training. To address the issue, Scheduled Sampling has been proposed. However, there are certain limitations in Scheduled Sampling and we propose two dynamic oracle-based methods to improve it. We manage to mitigate the discrepancy by changing the training process towards a less guided scheme and meanwhile aggregating the oracle’s demonstrations. Experimental results show that the proposed approaches improve translation quality over standard NMT system.
Tasks	Machine Translation
Published	2017-09-19
URL	http://arxiv.org/abs/1709.06265v2
PDF	http://arxiv.org/pdf/1709.06265v2.pdf
PWC	https://paperswithcode.com/paper/dynamic-oracle-for-neural-machine-translation
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A comment on the paper Prediction of Kidney Function from Biopsy Images using Convolutional Neural Networks


Title	A comment on the paper Prediction of Kidney Function from Biopsy Images using Convolutional Neural Networks
Authors	Washington LC dos-Santos, Angelo A Duarte, Luiz AR de Freitas
Abstract	This letter presente a comment on the paper Prediction of Kidney Function from Biopsy Images using Convolutional Neural Networks by Ledbetter et al. (2017)
Tasks
Published	2017-07-23
URL	http://arxiv.org/abs/1707.09869v1
PDF	http://arxiv.org/pdf/1707.09869v1.pdf
PWC	https://paperswithcode.com/paper/a-comment-on-the-paper-prediction-of-kidney
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EliXa: A Modular and Flexible ABSA Platform


Title	EliXa: A Modular and Flexible ABSA Platform
Authors	Iñaki San Vicente, Xabier Saralegi, Rodrigo Agerri
Abstract	This paper presents a supervised Aspect Based Sentiment Analysis (ABSA) system. Our aim is to develop a modular platform which allows to easily conduct experiments by replacing the modules or adding new features. We obtain the best result in the Opinion Target Extraction (OTE) task (slot 2) using an off-the-shelf sequence labeler. The target polarity classification (slot 3) is addressed by means of a multiclass SVM algorithm which includes lexical based features such as the polarity values obtained from domain and open polarity lexicons. The system obtains accuracies of 0.70 and 0.73 for the restaurant and laptop domain respectively, and performs second best in the out-of-domain hotel, achieving an accuracy of 0.80.
Tasks	Aspect-Based Sentiment Analysis, Sentiment Analysis
Published	2017-02-07
URL	http://arxiv.org/abs/1702.01944v1
PDF	http://arxiv.org/pdf/1702.01944v1.pdf
PWC	https://paperswithcode.com/paper/elixa-a-modular-and-flexible-absa-platform
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Multilayer Perceptron Algebra


Title	Multilayer Perceptron Algebra
Authors	Zhao Peng
Abstract	Artificial Neural Networks(ANN) has been phenomenally successful on various pattern recognition tasks. However, the design of neural networks rely heavily on the experience and intuitions of individual developers. In this article, the author introduces a mathematical structure called MLP algebra on the set of all Multilayer Perceptron Neural Networks(MLP), which can serve as a guiding principle to build MLPs accommodating to the particular data sets, and to build complex MLPs from simpler ones.
Tasks
Published	2017-01-18
URL	http://arxiv.org/abs/1701.04968v1
PDF	http://arxiv.org/pdf/1701.04968v1.pdf
PWC	https://paperswithcode.com/paper/multilayer-perceptron-algebra
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Pay Attention to Those Sets! Learning Quantification from Images


Title	Pay Attention to Those Sets! Learning Quantification from Images
Authors	Ionut Sorodoc, Sandro Pezzelle, Aurélie Herbelot, Mariella Dimiccoli, Raffaella Bernardi
Abstract	Major advances have recently been made in merging language and vision representations. But most tasks considered so far have confined themselves to the processing of objects and lexicalised relations amongst objects (content words). We know, however, that humans (even pre-school children) can abstract over raw data to perform certain types of higher-level reasoning, expressed in natural language by function words. A case in point is given by their ability to learn quantifiers, i.e. expressions like ‘few’, ‘some’ and ‘all’. From formal semantics and cognitive linguistics, we know that quantifiers are relations over sets which, as a simplification, we can see as proportions. For instance, in ‘most fish are red’, most encodes the proportion of fish which are red fish. In this paper, we study how well current language and vision strategies model such relations. We show that state-of-the-art attention mechanisms coupled with a traditional linguistic formalisation of quantifiers gives best performance on the task. Additionally, we provide insights on the role of ‘gist’ representations in quantification. A ‘logical’ strategy to tackle the task would be to first obtain a numerosity estimation for the two involved sets and then compare their cardinalities. We however argue that precisely identifying the composition of the sets is not only beyond current state-of-the-art models but perhaps even detrimental to a task that is most efficiently performed by refining the approximate numerosity estimator of the system.
Tasks
Published	2017-04-10
URL	http://arxiv.org/abs/1704.02923v1
PDF	http://arxiv.org/pdf/1704.02923v1.pdf
PWC	https://paperswithcode.com/paper/pay-attention-to-those-sets-learning
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Constrained Bayesian Optimization with Noisy Experiments


Title	Constrained Bayesian Optimization with Noisy Experiments
Authors	Benjamin Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy
Abstract	Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems. Data in these tests may be difficult to collect and outcomes may have high variance, resulting in potentially large measurement error. Bayesian optimization is a promising technique for efficiently optimizing multiple continuous parameters, but existing approaches degrade in performance when the noise level is high, limiting its applicability to many randomized experiments. We derive an expression for expected improvement under greedy batch optimization with noisy observations and noisy constraints, and develop a quasi-Monte Carlo approximation that allows it to be efficiently optimized. Simulations with synthetic functions show that optimization performance on noisy, constrained problems outperforms existing methods. We further demonstrate the effectiveness of the method with two real-world experiments conducted at Facebook: optimizing a ranking system, and optimizing server compiler flags.
Tasks
Published	2017-06-21
URL	http://arxiv.org/abs/1706.07094v2
PDF	http://arxiv.org/pdf/1706.07094v2.pdf
PWC	https://paperswithcode.com/paper/constrained-bayesian-optimization-with-noisy
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An optimal hierarchical clustering approach to segmentation of mobile LiDAR point clouds


Title	An optimal hierarchical clustering approach to segmentation of mobile LiDAR point clouds
Authors	Sheng Xu, Ruisheng Wang, Han Zheng
Abstract	This paper proposes a hierarchical clustering approach for the segmentation of mobile LiDAR point clouds. We perform the hierarchical clustering on unorganized point clouds based on a proximity matrix. The dissimilarity measure in the proximity matrix is calculated by the Euclidean distances between clusters and the difference of normal vectors at given points. The main contribution of this paper is that we succeed to optimize the combination of clusters in the hierarchical clustering. The combination is obtained by achieving the matching of a bipartite graph, and optimized by solving the minimum-cost perfect matching. Results show that the proposed optimal hierarchical clustering (OHC) succeeds to achieve the segmentation of multiple individual objects automatically and outperforms the state-of-the-art LiDAR point cloud segmentation approaches.
Tasks
Published	2017-03-06
URL	http://arxiv.org/abs/1703.02150v2
PDF	http://arxiv.org/pdf/1703.02150v2.pdf
PWC	https://paperswithcode.com/paper/an-optimal-hierarchical-clustering-approach
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