Paper Group ANR 818
Hyperspectral Unmixing Based on Clustered Multitask Networks. Tell Me Why Is It So? Explaining Knowledge Graph Relationships by Finding Descriptive Support Passages. RGB-D SLAM in Dynamic Environments Using Points Correlations. High frame-rate cardiac ultrasound imaging with deep learning. A Survey of Modern Object Detection Literature using Deep L …
Hyperspectral Unmixing Based on Clustered Multitask Networks
| Title | Hyperspectral Unmixing Based on Clustered Multitask Networks |
| Authors | Sara Khoshsokhan, Roozbeh Rajabi, Hadi Zayyani |
| Abstract | Hyperspectral remote sensing is a prominent research topic in data processing. Most of the spectral unmixing algorithms are developed by adopting the linear mixing models. Nonnegative matrix factorization (NMF) and its developments are used widely for estimation of signatures and fractional abundances in the SU problem. Sparsity constraints was added to NMF, and was regularized by $ L_ {q} $ norm. In this paper, at first hyperspectral images are clustered by fuzzy c- means method, and then a new algorithm based on sparsity constrained distributed optimization is used for spectral unmixing. In the proposed algorithm, a network including clusters is employed. Each pixel in the hyperspectral images considered as a node in this network. The proposed algorithm is optimized with diffusion LMS strategy, and then the update equations for fractional abundance and signature matrices are obtained. Simulation results based on defined performance metrics illustrate advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods. |
| Tasks | Distributed Optimization, Hyperspectral Unmixing |
| Published | 2018-12-27 |
| URL | http://arxiv.org/abs/1812.10788v1 |
| http://arxiv.org/pdf/1812.10788v1.pdf | |
| PWC | https://paperswithcode.com/paper/hyperspectral-unmixing-based-on-clustered |
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Tell Me Why Is It So? Explaining Knowledge Graph Relationships by Finding Descriptive Support Passages
| Title | Tell Me Why Is It So? Explaining Knowledge Graph Relationships by Finding Descriptive Support Passages |
| Authors | Sumit Bhatia, Purusharth Dwivedi, Avneet Kaur |
| Abstract | We address the problem of finding descriptive explanations of facts stored in a knowledge graph. This is important in high-risk domains such as healthcare, intelligence, etc. where users need additional information for decision making and is especially crucial for applications that rely on automatically constructed knowledge bases where machine learned systems extract facts from an input corpus and working of the extractors is opaque to the end-user. We follow an approach inspired from information retrieval and propose a simple and efficient, yet effective solution that takes into account passage level as well as document level properties to produce a ranked list of passages describing a given input relation. We test our approach using Wikidata as the knowledge base and Wikipedia as the source corpus and report results of user studies conducted to study the effectiveness of our proposed model. |
| Tasks | Decision Making, Information Retrieval |
| Published | 2018-03-17 |
| URL | http://arxiv.org/abs/1803.06555v1 |
| http://arxiv.org/pdf/1803.06555v1.pdf | |
| PWC | https://paperswithcode.com/paper/tell-me-why-is-it-so-explaining-knowledge |
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RGB-D SLAM in Dynamic Environments Using Points Correlations
| Title | RGB-D SLAM in Dynamic Environments Using Points Correlations |
| Authors | Weichen Dai, Yu Zhang, Ping Li, Zheng Fang |
| Abstract | This paper proposed a novel RGB-D SLAM method for dynamic environments. It follows traditional feature-based SLAM methods and utilizes a feature groups segmentation method to resist the disturbance caused by the dynamic objects using points correlations. The correlations between map points represented with a sparse graph are created by Delaunay triangulation. After removing non-consistency connections, the dynamic objects are separated from static background. The features only in the static map are used for motion estimation and bundle adjustment which improves the accuracy and robustness of SLAM in dynamic environments. The effectiveness of the proposed SLAM are evaluated using TUM RGB-D benchmark. The experiments demonstrate that the dynamic features are successfully removed and the system work perfectly in both low and high dynamic environments. The comparisons between proposed method and state-of-the-art visual systems clearly show that the comparable accurate results are achieved in low dynamic environments and the performance is improved significantly in high dynamic environments. |
| Tasks | Motion Estimation |
| Published | 2018-11-08 |
| URL | http://arxiv.org/abs/1811.03217v1 |
| http://arxiv.org/pdf/1811.03217v1.pdf | |
| PWC | https://paperswithcode.com/paper/rgb-d-slam-in-dynamic-environments-using |
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High frame-rate cardiac ultrasound imaging with deep learning
| Title | High frame-rate cardiac ultrasound imaging with deep learning |
| Authors | Ortal Senouf, Sanketh Vedula, Grigoriy Zurakhov, Alex M. Bronstein, Michael Zibulevsky, Oleg Michailovich, Dan Adam, David Blondheim |
| Abstract | Cardiac ultrasound imaging requires a high frame rate in order to capture rapid motion. This can be achieved by multi-line acquisition (MLA), where several narrow-focused received lines are obtained from each wide-focused transmitted line. This shortens the acquisition time at the expense of introducing block artifacts. In this paper, we propose a data-driven learning-based approach to improve the MLA image quality. We train an end-to-end convolutional neural network on pairs of real ultrasound cardiac data, acquired through MLA and the corresponding single-line acquisition (SLA). The network achieves a significant improvement in image quality for both $5-$ and $7-$line MLA resulting in a decorrelation measure similar to that of SLA while having the frame rate of MLA. |
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| Published | 2018-08-23 |
| URL | http://arxiv.org/abs/1808.07823v1 |
| http://arxiv.org/pdf/1808.07823v1.pdf | |
| PWC | https://paperswithcode.com/paper/high-frame-rate-cardiac-ultrasound-imaging |
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A Survey of Modern Object Detection Literature using Deep Learning
| Title | A Survey of Modern Object Detection Literature using Deep Learning |
| Authors | Karanbir Singh Chahal, Kuntal Dey |
| Abstract | Object detection is the identification of an object in the image along with its localisation and classification. It has wide spread applications and is a critical component for vision based software systems. This paper seeks to perform a rigorous survey of modern object detection algorithms that use deep learning. As part of the survey, the topics explored include various algorithms, quality metrics, speed/size trade offs and training methodologies. This paper focuses on the two types of object detection algorithms- the SSD class of single step detectors and the Faster R-CNN class of two step detectors. Techniques to construct detectors that are portable and fast on low powered devices are also addressed by exploring new lightweight convolutional base architectures. Ultimately, a rigorous review of the strengths and weaknesses of each detector leads us to the present state of the art. |
| Tasks | Object Detection |
| Published | 2018-08-22 |
| URL | http://arxiv.org/abs/1808.07256v1 |
| http://arxiv.org/pdf/1808.07256v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-survey-of-modern-object-detection |
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Efficient online learning for large-scale peptide identification
| Title | Efficient online learning for large-scale peptide identification |
| Authors | Xijun Liang, Zhonghang Xia, Yongxiang Wang, Ling Jian, Xinnan Niu, Andrew Link |
| Abstract | Motivation: Post-database searching is a key procedure in peptide dentification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve the accuracy of peptide identification, the challenge remains on large-scale datasets and datasets with an extremely large proportion of false positives (hard datasets). A more efficient learning strategy is required for improving the performance of peptide identification on challenging datasets. Results: In this work, we present an online learning method to conquer the challenges remained for exiting peptide identification algorithms. We propose a cost-sensitive learning model by using different loss functions for decoy and target PSMs respectively. A larger penalty for wrongly selecting decoy PSMs than that for target PSMs, and thus the new model can reduce its false discovery rate on hard datasets. Also, we design an online learning algorithm, OLCS-Ranker, to solve the proposed learning model. Rather than taking all training data samples all at once, OLCS-Ranker iteratively feeds in only one training sample into the learning model at each round. As a result, the memory requirement is significantly reduced for large-scale problems. Experimental studies show that OLCS-Ranker outperforms benchmark methods, such as CRanker and Batch-CS-Ranker, in terms of accuracy and stability. Furthermore, OLCS-Ranker is 15–85 times faster than CRanker method on large datasets. Availability and implementation: OLCS-Ranker software is available at no charge for non-commercial use at https://github.com/Isaac-QiXing/CRanker. |
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| Published | 2018-05-08 |
| URL | http://arxiv.org/abs/1805.03006v1 |
| http://arxiv.org/pdf/1805.03006v1.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-online-learning-for-large-scale |
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Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity
| Title | Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity |
| Authors | Tatsumi Uezato, Mathieu Fauvel, Nicolas Dobigeon |
| Abstract | Spectral variability is one of the major issue when conducting hyperspectral unmixing. Within a given image composed of some elementary materials (herein referred to as endmember classes), the spectral signature characterizing these classes may spatially vary due to intrinsic component fluctuations or external factors (illumination). These redundant multiple endmember spectra within each class adversely affect the performance of unmixing methods. This paper proposes a mixing model that explicitly incorporates a hierarchical structure of redundant multiple spectra representing each class. The proposed method is designed to promote sparsity on the selection of both spectra and classes within each pixel. The resulting unmixing algorithm is able to adaptively recover several bundles of endmember spectra associated with each class and robustly estimate abundances. In addition, its flexibility allows a variable number of classes to be present within each pixel of the hyperspectral image to be unmixed. The proposed method is compared with other state-of-the-art unmixing methods that incorporate sparsity using both simulated and real hyperspectral data. The results show that the proposed method can successfully determine the variable number of classes present within each class and estimate the corresponding class abundances. |
| Tasks | Hyperspectral Unmixing |
| Published | 2018-04-30 |
| URL | http://arxiv.org/abs/1804.11132v1 |
| http://arxiv.org/pdf/1804.11132v1.pdf | |
| PWC | https://paperswithcode.com/paper/hyperspectral-unmixing-with-spectral |
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Learning Latent Events from Network Message Logs
| Title | Learning Latent Events from Network Message Logs |
| Authors | Siddhartha Satpathi, Supratim Deb, R Srikant, He Yan |
| Abstract | We consider the problem of separating error messages generated in large distributed data center networks into error events. In such networks, each error event leads to a stream of messages generated by hardware and software components affected by the event. These messages are stored in a giant message log. We consider the unsupervised learning problem of identifying the signatures of events that generated these messages; here, the signature of an error event refers to the mixture of messages generated by the event. One of the main contributions of the paper is a novel mapping of our problem which transforms it into a problem of topic discovery in documents. Events in our problem correspond to topics and messages in our problem correspond to words in the topic discovery problem. However, there is no direct analog of documents. Therefore, we use a non-parametric change-point detection algorithm, which has linear computational complexity in the number of messages, to divide the message log into smaller subsets called episodes, which serve as the equivalents of documents. After this mapping has been done, we use a well-known algorithm for topic discovery, called LDA, to solve our problem. We theoretically analyze the change-point detection algorithm, and show that it is consistent and has low sample complexity. We also demonstrate the scalability of our algorithm on a real data set consisting of $97$ million messages collected over a period of $15$ days, from a distributed data center network which supports the operations of a large wireless service provider. |
| Tasks | Change Point Detection |
| Published | 2018-04-10 |
| URL | https://arxiv.org/abs/1804.03346v3 |
| https://arxiv.org/pdf/1804.03346v3.pdf | |
| PWC | https://paperswithcode.com/paper/learning-latent-events-from-network-message |
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Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection
| Title | Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection |
| Authors | Luke R Harries, Suyi Zhang, Geoffroy Dubourg-Felonneau, James H R Farmery, Jonathan Sinai, Belle Taylor, Nirmesh Patel, John W Cassidy, John Shawe-Taylor, Harry W Clifford |
| Abstract | DNA sequencing to identify genetic variants is becoming increasingly valuable in clinical settings. Assessment of variants in such sequencing data is commonly implemented through Bayesian heuristic algorithms. Machine learning has shown great promise in improving on these variant calls, but the input for these is still a standardized “pile-up” image, which is not always best suited. In this paper, we present a novel method for generating images from DNA sequencing data, which interlaces the human reference genome with personalized sequencing output, to maximize usage of sequencing reads and improve machine learning algorithm performance. We demonstrate the success of this in improving standard germline variant calling. We also furthered this approach to include somatic variant calling across tumor/normal data with Siamese networks. These approaches can be used in machine learning applications on sequencing data with the hope of improving clinical outcomes, and are freely available for noncommercial use at www.ccg.ai. |
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| Published | 2018-11-26 |
| URL | http://arxiv.org/abs/1811.11674v1 |
| http://arxiv.org/pdf/1811.11674v1.pdf | |
| PWC | https://paperswithcode.com/paper/interlacing-personal-and-reference-genomes |
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Hierarchical Deep Co-segmentation of Primary Objects in Aerial Videos
| Title | Hierarchical Deep Co-segmentation of Primary Objects in Aerial Videos |
| Authors | Jia Li, Pengcheng Yuan, Daxin Gu, Yonghong Tian |
| Abstract | Primary object segmentation plays an important role in understanding videos generated by unmanned aerial vehicles. In this paper, we propose a large-scale dataset with 500 aerial videos and manually annotated primary objects. To the best of our knowledge, it is the largest dataset to date for primary object segmentation in aerial videos. From this dataset, we find most aerial videos contain large-scale scenes, small primary objects as well as consistently varying scales and viewpoints. Inspired by that, we propose a hierarchical deep co-segmentation approach that repeatedly divides a video into two sub-videos formed by the odd and even frames, respectively. In this manner, the primary objects shared by sub-videos can be co-segmented by training two-stream CNNs and finally refined within the neighborhood reversible flows. Experimental results show that our approach remarkably outperforms 17 state-of-the-art methods in segmenting primary objects in various types of aerial videos. |
| Tasks | Semantic Segmentation |
| Published | 2018-06-27 |
| URL | http://arxiv.org/abs/1806.10274v2 |
| http://arxiv.org/pdf/1806.10274v2.pdf | |
| PWC | https://paperswithcode.com/paper/hierarchical-deep-co-segmentation-of-primary |
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Conditioning Deep Generative Raw Audio Models for Structured Automatic Music
| Title | Conditioning Deep Generative Raw Audio Models for Structured Automatic Music |
| Authors | Rachel Manzelli, Vijay Thakkar, Ali Siahkamari, Brian Kulis |
| Abstract | Existing automatic music generation approaches that feature deep learning can be broadly classified into two types: raw audio models and symbolic models. Symbolic models, which train and generate at the note level, are currently the more prevalent approach; these models can capture long-range dependencies of melodic structure, but fail to grasp the nuances and richness of raw audio generations. Raw audio models, such as DeepMind’s WaveNet, train directly on sampled audio waveforms, allowing them to produce realistic-sounding, albeit unstructured music. In this paper, we propose an automatic music generation methodology combining both of these approaches to create structured, realistic-sounding compositions. We consider a Long Short Term Memory network to learn the melodic structure of different styles of music, and then use the unique symbolic generations from this model as a conditioning input to a WaveNet-based raw audio generator, creating a model for automatic, novel music. We then evaluate this approach by showcasing results of this work. |
| Tasks | Music Generation |
| Published | 2018-06-26 |
| URL | http://arxiv.org/abs/1806.09905v1 |
| http://arxiv.org/pdf/1806.09905v1.pdf | |
| PWC | https://paperswithcode.com/paper/conditioning-deep-generative-raw-audio-models |
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Minimal Paths for Tubular Structure Segmentation with Coherence Penalty and Adaptive Anisotropy
| Title | Minimal Paths for Tubular Structure Segmentation with Coherence Penalty and Adaptive Anisotropy |
| Authors | Da Chen, Jiong Zhang, Laurent D. Cohen |
| Abstract | The minimal path method has proven to be particularly useful and efficient in tubular structure segmentation applications. In this paper, we propose a new minimal path model associated with a dynamic Riemannian metric embedded with an appearance feature coherence penalty and an adaptive anisotropy enhancement term. The features that characterize the appearance and anisotropy properties of a tubular structure are extracted through the associated orientation score. The proposed dynamic Riemannian metric is updated in the course of the geodesic distance computation carried out by the efficient single-pass fast marching method. Compared to state-of-the-art minimal path models, the proposed minimal path model is able to extract the desired tubular structures from a complicated vessel tree structure. In addition, we propose an efficient prior path-based method to search for vessel radius value at each centerline position of the target. Finally, we perform the numerical experiments on both synthetic and real images. The quantitive validation is carried out on retinal vessel images. The results indicate that the proposed model indeed achieves a promising performance. |
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| Published | 2018-09-21 |
| URL | http://arxiv.org/abs/1809.07987v4 |
| http://arxiv.org/pdf/1809.07987v4.pdf | |
| PWC | https://paperswithcode.com/paper/minimal-paths-for-tubular-structure |
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Training Passive Photonic Reservoirs with Integrated Optical Readout
| Title | Training Passive Photonic Reservoirs with Integrated Optical Readout |
| Authors | Matthias Freiberger, Andrew Katumba, Peter Bienstman, Joni Dambre |
| Abstract | As Moore’s law comes to an end, neuromorphic approaches to computing are on the rise. One of these, passive photonic reservoir computing, is a strong candidate for computing at high bitrates (> 10 Gbps) and with low energy consumption. Currently though, both benefits are limited by the necessity to perform training and readout operations in the electrical domain. Thus, efforts are currently underway in the photonic community to design an integrated optical readout, which allows to perform all operations in the optical domain. In addition to the technological challenge of designing such a readout, new algorithms have to be designed in order to train it. Foremost, suitable algorithms need to be able to deal with the fact that the actual on-chip reservoir states are not directly observable. In this work, we investigate several options for such a training algorithm and propose a solution in which the complex states of the reservoir can be observed by appropriately setting the readout weights, while iterating over a predefined input sequence. We perform numerical simulations in order to compare our method with an ideal baseline requiring full observability as well as with an established black-box optimization approach (CMA-ES). |
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| Published | 2018-10-08 |
| URL | http://arxiv.org/abs/1810.03377v1 |
| http://arxiv.org/pdf/1810.03377v1.pdf | |
| PWC | https://paperswithcode.com/paper/training-passive-photonic-reservoirs-with |
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SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification
| Title | SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification |
| Authors | Ruimao Zhang, Hongbin Sun, Jingyu Li, Yuying Ge, Liang Lin, Ping Luo, Xiaogang Wang |
| Abstract | Video person re-identification attracts much attention in recent years. It aims to match image sequences of pedestrians from different camera views. Previous approaches usually improve this task from three aspects, including a) selecting more discriminative frames, b) generating more informative temporal representations, and c) developing more effective distance metrics. To address the above issues, we present a novel and practical deep architecture for video person re-identification termed Self-and-Collaborative Attention Network (SCAN). It has several appealing properties. First, SCAN adopts non-parametric attention mechanism to refine the intra-sequence and inter-sequence feature representation of videos, and outputs self-and-collaborative feature representation for each video, making the discriminative frames aligned between the probe and gallery sequences.Second, beyond existing models, a generalized pairwise similarity measurement is proposed to calculate the similarity feature representations of video pairs, enabling computing the matching scores by the binary classifier. Third, a dense clip segmentation strategy is also introduced to generate rich probe-gallery pairs to optimize the model. Extensive experiments demonstrate the effectiveness of SCAN, which outperforms the best-performing baselines on iLIDS-VID, PRID2011 and MARS dataset, respectively. |
| Tasks | Person Re-Identification, Video-Based Person Re-Identification |
| Published | 2018-07-16 |
| URL | https://arxiv.org/abs/1807.05688v4 |
| https://arxiv.org/pdf/1807.05688v4.pdf | |
| PWC | https://paperswithcode.com/paper/scan-self-and-collaborative-attention-network |
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Nested cross-validation when selecting classifiers is overzealous for most practical applications
| Title | Nested cross-validation when selecting classifiers is overzealous for most practical applications |
| Authors | Jacques Wainer, Gavin Cawley |
| Abstract | When selecting a classification algorithm to be applied to a particular problem, one has to simultaneously select the best algorithm for that dataset \emph{and} the best set of hyperparameters for the chosen model. The usual approach is to apply a nested cross-validation procedure; hyperparameter selection is performed in the inner cross-validation, while the outer cross-validation computes an unbiased estimate of the expected accuracy of the algorithm \emph{with cross-validation based hyperparameter tuning}. The alternative approach, which we shall call `flat cross-validation’, uses a single cross-validation step both to select the optimal hyperparameter values and to provide an estimate of the expected accuracy of the algorithm, that while biased may nevertheless still be used to select the best learning algorithm. We tested both procedures using 12 different algorithms on 115 real life binary datasets and conclude that using the less computationally expensive flat cross-validation procedure will generally result in the selection of an algorithm that is, for all practical purposes, of similar quality to that selected via nested cross-validation, provided the learning algorithms have relatively few hyperparameters to be optimised. | |
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| Published | 2018-09-25 |
| URL | http://arxiv.org/abs/1809.09446v1 |
| http://arxiv.org/pdf/1809.09446v1.pdf | |
| PWC | https://paperswithcode.com/paper/nested-cross-validation-when-selecting |
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