Paper Group ANR 755
Robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks. Learning Noun Cases Using Sequential Neural Networks. Exploring Visual Relationship for Image Captioning. Optimizing Waiting Thresholds Within A State Machine. Image Segmentation and Processing for Efficient Parking Space Analysis. FuzzerGym: …
Robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks
| Title | Robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks |
| Authors | Drew Linsley, Junkyung Kim, David Berson, Thomas Serre |
| Abstract | Recent successes in deep learning have started to impact neuroscience. Of particular significance are claims that current segmentation algorithms achieve “super-human” accuracy in an area known as connectomics. However, as we will show, these algorithms do not effectively generalize beyond the particular source and brain tissues used for training – severely limiting their usability by the broader neuroscience community. To fill this gap, we describe a novel connectomics challenge for source- and tissue-agnostic reconstruction of neurons (STAR), which favors broad generalization over fitting specific datasets. We first demonstrate that current state-of-the-art approaches to neuron segmentation perform poorly on the challenge. We further describe a novel convolutional recurrent neural network module that combines short-range horizontal connections within a processing stage and long-range top-down connections between stages. The resulting architecture establishes the state of the art on the STAR challenge and represents a significant step towards widely usable and fully-automated connectomics analysis. |
| Tasks | Cell Segmentation, Contour Detection, Semantic Segmentation |
| Published | 2018-11-28 |
| URL | https://arxiv.org/abs/1811.11356v4 |
| https://arxiv.org/pdf/1811.11356v4.pdf | |
| PWC | https://paperswithcode.com/paper/robust-neural-circuit-reconstruction-from |
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Learning Noun Cases Using Sequential Neural Networks
| Title | Learning Noun Cases Using Sequential Neural Networks |
| Authors | Sina Ahmadi |
| Abstract | Morphological declension, which aims to inflect nouns to indicate number, case and gender, is an important task in natural language processing (NLP). This research proposal seeks to address the degree to which Recurrent Neural Networks (RNNs) are efficient in learning to decline noun cases. Given the challenge of data sparsity in processing morphologically rich languages and also, the flexibility of sentence structures in such languages, we believe that modeling morphological dependencies can improve the performance of neural network models. It is suggested to carry out various experiments to understand the interpretable features that may lead to a better generalization of the learned models on cross-lingual tasks. |
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| Published | 2018-10-09 |
| URL | http://arxiv.org/abs/1810.03996v1 |
| http://arxiv.org/pdf/1810.03996v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-noun-cases-using-sequential-neural |
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Exploring Visual Relationship for Image Captioning
| Title | Exploring Visual Relationship for Image Captioning |
| Authors | Ting Yao, Yingwei Pan, Yehao Li, Tao Mei |
| Abstract | It is always well believed that modeling relationships between objects would be helpful for representing and eventually describing an image. Nevertheless, there has not been evidence in support of the idea on image description generation. In this paper, we introduce a new design to explore the connections between objects for image captioning under the umbrella of attention-based encoder-decoder framework. Specifically, we present Graph Convolutional Networks plus Long Short-Term Memory (dubbed as GCN-LSTM) architecture that novelly integrates both semantic and spatial object relationships into image encoder. Technically, we build graphs over the detected objects in an image based on their spatial and semantic connections. The representations of each region proposed on objects are then refined by leveraging graph structure through GCN. With the learnt region-level features, our GCN-LSTM capitalizes on LSTM-based captioning framework with attention mechanism for sentence generation. Extensive experiments are conducted on COCO image captioning dataset, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, GCN-LSTM increases CIDEr-D performance from 120.1% to 128.7% on COCO testing set. |
| Tasks | Image Captioning |
| Published | 2018-09-19 |
| URL | http://arxiv.org/abs/1809.07041v1 |
| http://arxiv.org/pdf/1809.07041v1.pdf | |
| PWC | https://paperswithcode.com/paper/exploring-visual-relationship-for-image |
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Optimizing Waiting Thresholds Within A State Machine
| Title | Optimizing Waiting Thresholds Within A State Machine |
| Authors | Rohit Pandey, Yifan Chang, Cameron White, Gaurav Jagtiani, Aerin Young Kim, Gil Lapid Shafriri, Sathya Singh |
| Abstract | Azure (the cloud service provided by Microsoft) is composed of physical computing units which are called nodes. These nodes are controlled by a software component called Fabric Controller (FC), which can consider the nodes to be in one of many different states such as Ready, Unhealthy, Booting, etc. Some of these states correspond to a node being unresponsive to FCs requests. When a node goes unresponsive for more than a set threshold, FC intervenes and reboots the node. We minimized the downtime caused by the intervention threshold when a node switches to the Unhealthy state by fitting various heavy-tail probability distributions. We consider using features of the node to customize the organic recovery model to the individual nodes that go unhealthy. This regression approach allows us to use information about the node like hardware, software versions, historical performance indicators, etc. to inform the organic recovery model and hence the optimal threshold. In another direction, we consider generalizing this to an arbitrary number of thresholds within the node state machine (or Markov chain). When the states become intertwined in ways that different thresholds start affecting each other, we can’t simply optimize each of them in isolation. For best results, we must consider this as an optimization problem in many variables (the number of thresholds). We no longer have a nice closed form solution for this more complex problem like we did with one threshold, but we can still use numerical techniques (gradient descent) to solve it. |
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| Published | 2018-10-08 |
| URL | http://arxiv.org/abs/1810.03278v1 |
| http://arxiv.org/pdf/1810.03278v1.pdf | |
| PWC | https://paperswithcode.com/paper/optimizing-waiting-thresholds-within-a-state |
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Image Segmentation and Processing for Efficient Parking Space Analysis
| Title | Image Segmentation and Processing for Efficient Parking Space Analysis |
| Authors | Chetan Sai Tutika, Charan Vallapaneni, Karthik R, Bharath KP, N Ruban Rajesh Kumar Muthu |
| Abstract | In this paper, we develop a method to detect vacant parking spaces in an environment with unclear segments and contours with the help of MATLAB image processing capabilities. Due to the anomalies present in the parking spaces, such as uneven illumination, distorted slot lines and overlapping of cars. The present-day conventional algorithms have difficulties processing the image for accurate results. The algorithm proposed uses a combination of image pre-processing and false contour detection techniques to improve the detection efficiency. The proposed method also eliminates the need to employ individual sensors to detect a car, instead uses real-time static images to consider a group of slots together, instead of the usual single slot method. This greatly decreases the expenses required to design an efficient parking system. We compare the performance of our algorithm to that of other techniques. These comparisons show that the proposed algorithm can detect the vacancies in the parking spots while ignoring the false data and other distortions. |
| Tasks | Contour Detection, Semantic Segmentation |
| Published | 2018-03-13 |
| URL | http://arxiv.org/abs/1803.04620v1 |
| http://arxiv.org/pdf/1803.04620v1.pdf | |
| PWC | https://paperswithcode.com/paper/image-segmentation-and-processing-for |
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FuzzerGym: A Competitive Framework for Fuzzing and Learning
| Title | FuzzerGym: A Competitive Framework for Fuzzing and Learning |
| Authors | William Drozd, Michael D. Wagner |
| Abstract | Fuzzing is a commonly used technique designed to test software by automatically crafting program inputs. Currently, the most successful fuzzing algorithms emphasize simple, low-overhead strategies with the ability to efficiently monitor program state during execution. Through compile-time instrumentation, these approaches have access to numerous aspects of program state including coverage, data flow, and heterogeneous fault detection and classification. However, existing approaches utilize blind random mutation strategies when generating test inputs. We present a different approach that uses this state information to optimize mutation operators using reinforcement learning (RL). By integrating OpenAI Gym with libFuzzer we are able to simultaneously leverage advancements in reinforcement learning as well as fuzzing to achieve deeper coverage across several varied benchmarks. Our technique connects the rich, efficient program monitors provided by LLVM Santizers with a deep neural net to learn mutation selection strategies directly from the input data. The cross-language, asynchronous architecture we developed enables us to apply any OpenAI Gym compatible deep reinforcement learning algorithm to any fuzzing problem with minimal slowdown. |
| Tasks | Fault Detection |
| Published | 2018-07-19 |
| URL | http://arxiv.org/abs/1807.07490v1 |
| http://arxiv.org/pdf/1807.07490v1.pdf | |
| PWC | https://paperswithcode.com/paper/fuzzergym-a-competitive-framework-for-fuzzing |
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Nonlinear Regression without i.i.d. Assumption
| Title | Nonlinear Regression without i.i.d. Assumption |
| Authors | Qing Xu, Xiaohua Xuan |
| Abstract | In this paper, we consider a class of nonlinear regression problems without the assumption of being independent and identically distributed. We propose a correspondent mini-max problem for nonlinear regression and give a numerical algorithm. Such an algorithm can be applied in regression and machine learning problems, and yield better results than traditional least square and machine learning methods. |
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| Published | 2018-11-23 |
| URL | http://arxiv.org/abs/1811.09623v2 |
| http://arxiv.org/pdf/1811.09623v2.pdf | |
| PWC | https://paperswithcode.com/paper/nonlinear-regression-without-iid-assumption |
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Construction of all-in-focus images assisted by depth sensing
| Title | Construction of all-in-focus images assisted by depth sensing |
| Authors | Hang Liu, Hengyu Li, Jun Luo, Shaorong Xie, Yu Sun |
| Abstract | Multi-focus image fusion is a technique for obtaining an all-in-focus image in which all objects are in focus to extend the limited depth of field (DoF) of an imaging system. Different from traditional RGB-based methods, this paper presents a new multi-focus image fusion method assisted by depth sensing. In this work, a depth sensor is used together with a color camera to capture images of a scene. A graph-based segmentation algorithm is used to segment the depth map from the depth sensor, and the segmented regions are used to guide a focus algorithm to locate in-focus image blocks from among multi-focus source images to construct the reference all-in-focus image. Five test scenes and six evaluation metrics were used to compare the proposed method and representative state-of-the-art algorithms. Experimental results quantitatively demonstrate that this method outperforms existing methods in both speed and quality (in terms of comprehensive fusion metrics). The generated images can potentially be used as reference all-in-focus images. |
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| Published | 2018-06-05 |
| URL | http://arxiv.org/abs/1806.01524v1 |
| http://arxiv.org/pdf/1806.01524v1.pdf | |
| PWC | https://paperswithcode.com/paper/construction-of-all-in-focus-images-assisted |
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Towards Game-based Metrics for Computational Co-creativity
| Title | Towards Game-based Metrics for Computational Co-creativity |
| Authors | Rodrigo Canaan, Stefan Menzel, Julian Togelius, Andy Nealen |
| Abstract | We propose the following question: what game-like interactive system would provide a good environment for measuring the impact and success of a co-creative, cooperative agent? Creativity is often formulated in terms of novelty, value, surprise and interestingness. We review how these concepts are measured in current computational intelligence research and provide a mapping from modern electronic and tabletop games to open research problems in mixed-initiative systems and computational co-creativity. We propose application scenarios for future research, and a number of metrics under which the performance of cooperative agents in these environments will be evaluated. |
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| Published | 2018-09-26 |
| URL | http://arxiv.org/abs/1809.09762v1 |
| http://arxiv.org/pdf/1809.09762v1.pdf | |
| PWC | https://paperswithcode.com/paper/towards-game-based-metrics-for-computational |
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Automatic localization and decoding of honeybee markers using deep convolutional neural networks
| Title | Automatic localization and decoding of honeybee markers using deep convolutional neural networks |
| Authors | Benjamin Wild, Leon Sixt, Tim Landgraf |
| Abstract | The honeybee is a fascinating model animal to investigate how collective behavior emerges from (inter-)actions of thousands of individuals. Bees may acquire unique memories throughout their lives. These experiences affect social interactions even over large time frames. Tracking and identifying all bees in the colony over their lifetimes therefore may likely shed light on the interplay of individual differences and colony behavior. This paper proposes a software pipeline based on two deep convolutional neural networks for the localization and decoding of custom binary markers that honeybees carry from their first to the last day in their life. We show that this approach outperforms similar systems proposed in recent literature. By opening this software for the public, we hope that the resulting datasets will help advancing the understanding of honeybee collective intelligence. |
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| Published | 2018-02-13 |
| URL | http://arxiv.org/abs/1802.04557v2 |
| http://arxiv.org/pdf/1802.04557v2.pdf | |
| PWC | https://paperswithcode.com/paper/automatic-localization-and-decoding-of |
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Using Search Queries to Understand Health Information Needs in Africa
| Title | Using Search Queries to Understand Health Information Needs in Africa |
| Authors | Rediet Abebe, Shawndra Hill, Jennifer Wortman Vaughan, Peter M. Small, H. Andrew Schwartz |
| Abstract | The lack of comprehensive, high-quality health data in developing nations creates a roadblock for combating the impacts of disease. One key challenge is understanding the health information needs of people in these nations. Without understanding people’s everyday needs, concerns, and misconceptions, health organizations and policymakers lack the ability to effectively target education and programming efforts. In this paper, we propose a bottom-up approach that uses search data from individuals to uncover and gain insight into health information needs in Africa. We analyze Bing searches related to HIV/AIDS, malaria, and tuberculosis from all 54 African nations. For each disease, we automatically derive a set of common search themes or topics, revealing a wide-spread interest in various types of information, including disease symptoms, drugs, concerns about breastfeeding, as well as stigma, beliefs in natural cures, and other topics that may be hard to uncover through traditional surveys. We expose the different patterns that emerge in health information needs by demographic groups (age and sex) and country. We also uncover discrepancies in the quality of content returned by search engines to users by topic. Combined, our results suggest that search data can help illuminate health information needs in Africa and inform discussions on health policy and targeted education efforts both on- and offline. |
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| Published | 2018-06-14 |
| URL | http://arxiv.org/abs/1806.05740v2 |
| http://arxiv.org/pdf/1806.05740v2.pdf | |
| PWC | https://paperswithcode.com/paper/using-search-queries-to-understand-health |
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Picking Apart Story Salads
| Title | Picking Apart Story Salads |
| Authors | Su Wang, Eric Holgate, Greg Durrett, Katrin Erk |
| Abstract | During natural disasters and conflicts, information about what happened is often confusing, messy, and distributed across many sources. We would like to be able to automatically identify relevant information and assemble it into coherent narratives of what happened. To make this task accessible to neural models, we introduce Story Salads, mixtures of multiple documents that can be generated at scale. By exploiting the Wikipedia hierarchy, we can generate salads that exhibit challenging inference problems. Story salads give rise to a novel, challenging clustering task, where the objective is to group sentences from the same narratives. We demonstrate that simple bag-of-words similarity clustering falls short on this task and that it is necessary to take into account global context and coherence. |
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| Published | 2018-10-31 |
| URL | http://arxiv.org/abs/1810.13391v1 |
| http://arxiv.org/pdf/1810.13391v1.pdf | |
| PWC | https://paperswithcode.com/paper/picking-apart-story-salads |
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Deep Visual Domain Adaptation: A Survey
| Title | Deep Visual Domain Adaptation: A Survey |
| Authors | Mei Wang, Weihong Deng |
| Abstract | Deep domain adaption has emerged as a new learning technique to address the lack of massive amounts of labeled data. Compared to conventional methods, which learn shared feature subspaces or reuse important source instances with shallow representations, deep domain adaption methods leverage deep networks to learn more transferable representations by embedding domain adaptation in the pipeline of deep learning. There have been comprehensive surveys for shallow domain adaption, but few timely reviews the emerging deep learning based methods. In this paper, we provide a comprehensive survey of deep domain adaptation methods for computer vision applications with four major contributions. First, we present a taxonomy of different deep domain adaption scenarios according to the properties of data that define how two domains are diverged. Second, we summarize deep domain adaption approaches into several categories based on training loss, and analyze and compare briefly the state-of-the-art methods under these categories. Third, we overview the computer vision applications that go beyond image classification, such as face recognition, semantic segmentation and object detection. Fourth, some potential deficiencies of current methods and several future directions are highlighted. |
| Tasks | Domain Adaptation, Face Recognition, Image Classification, Object Detection, Semantic Segmentation |
| Published | 2018-02-10 |
| URL | http://arxiv.org/abs/1802.03601v4 |
| http://arxiv.org/pdf/1802.03601v4.pdf | |
| PWC | https://paperswithcode.com/paper/deep-visual-domain-adaptation-a-survey |
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Parametric Synthesis of Text on Stylized Backgrounds using PGGANs
| Title | Parametric Synthesis of Text on Stylized Backgrounds using PGGANs |
| Authors | Mayank Gupta, Abhinav Kumar, Sriganesh Madhvanath |
| Abstract | We describe a novel method of generating high-resolution real-world images of text where the style and textual content of the images are described parametrically. Our method combines text to image retrieval techniques with progressive growing of Generative Adversarial Networks (PGGANs) to achieve conditional generation of photo-realistic images that reflect specific styles, as well as artifacts seen in real-world images. We demonstrate our method in the context of automotive license plates. We assess the impact of varying the number of training images of each style on the fidelity of the generated style, and demonstrate the quality of the generated images using license plate recognition systems. |
| Tasks | Image Retrieval, License Plate Recognition |
| Published | 2018-09-22 |
| URL | http://arxiv.org/abs/1809.08488v1 |
| http://arxiv.org/pdf/1809.08488v1.pdf | |
| PWC | https://paperswithcode.com/paper/parametric-synthesis-of-text-on-stylized |
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3D segmentation of mandible from multisectional CT scans by convolutional neural networks
| Title | 3D segmentation of mandible from multisectional CT scans by convolutional neural networks |
| Authors | Bingjiang Qiu, Jiapan Guo, J. Kraeima, R. J. H. Borra, M. J. H. Witjes, P. M. A. van Ooijen |
| Abstract | Segmentation of mandibles in CT scans during virtual surgical planning is crucial for 3D surgical planning in order to obtain a detailed surface representation of the patients bone. Automatic segmentation of mandibles in CT scans is a challenging task due to large variation in their shape and size between individuals. In order to address this challenge we propose a convolutional neural network approach for mandible segmentation in CT scans by considering the continuum of anatomical structures through different planes. The proposed convolutional neural network adopts the architecture of the U-Net and then combines the resulting 2D segmentations from three different planes into a 3D segmentation. We implement such a segmentation approach on 11 neck CT scans and then evaluate the performance. We achieve an average dice coefficient of $ 0.89 $ on two testing mandible segmentation. Experimental results show that our proposed approach for mandible segmentation in CT scans exhibits high accuracy. |
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| Published | 2018-09-18 |
| URL | http://arxiv.org/abs/1809.06752v1 |
| http://arxiv.org/pdf/1809.06752v1.pdf | |
| PWC | https://paperswithcode.com/paper/3d-segmentation-of-mandible-from |
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