Paper Group ANR 1025
Fitting IVIM with Variable Projection and Simplicial Optimization. Multimodal Emotion Classification. Spatio-Temporal Deep Graph Infomax. Classification of glomerular hypercellularity using convolutional features and support vector machine. Deep Patent Landscaping Model Using Transformer and Graph Embedding. Towards better social crisis data with H …
Fitting IVIM with Variable Projection and Simplicial Optimization
| Title | Fitting IVIM with Variable Projection and Simplicial Optimization |
| Authors | Shreyas Fadnavis, Hamza Farooq, Maryam Afzali, Christoph Lenglet, Tryphon Georgiou, Hu Cheng, Sharlene Newman, Shahnawaz Ahmed, Rafael Neto Henriques, Eric Peterson, Serge Koudoro, Ariel Rokem, Eleftherios Garyfallidis |
| Abstract | Fitting multi-exponential models to Diffusion MRI (dMRI) data has always been challenging due to various underlying complexities. In this work, we introduce a novel and robust fitting framework for the standard two-compartment IVIM microstructural model. This framework provides a significant improvement over the existing methods and helps estimate the associated diffusion and perfusion parameters of IVIM in an automatic manner. As a part of this work we provide capabilities to switch between more advanced global optimization methods such as simplicial homology (SH) and differential evolution (DE). Our experiments show that the results obtained from this simultaneous fitting procedure disentangle the model parameters in a reduced subspace. The proposed framework extends the seminal work originated in the MIX framework, with improved procedures for multi-stage fitting. This framework has been made available as an open-source Python implementation and disseminated to the community through the DIPY project. |
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| Published | 2019-09-27 |
| URL | https://arxiv.org/abs/1910.00095v3 |
| https://arxiv.org/pdf/1910.00095v3.pdf | |
| PWC | https://paperswithcode.com/paper/fitting-ivim-with-variable-projection-and |
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Multimodal Emotion Classification
| Title | Multimodal Emotion Classification |
| Authors | Anurag Illendula, Amit Sheth |
| Abstract | Most NLP and Computer Vision tasks are limited to scarcity of labelled data. In social media emotion classification and other related tasks, hashtags have been used as indicators to label data. With the rapid increase in emoji usage of social media, emojis are used as an additional feature for major social NLP tasks. However, this is less explored in case of multimedia posts on social media where posts are composed of both image and text. At the same time, w.e have seen a surge in the interest to incorporate domain knowledge to improve machine understanding of text. In this paper, we investigate whether domain knowledge for emoji can improve the accuracy of emotion classification task. We exploit the importance of different modalities from social media post for emotion classification task using state-of-the-art deep learning architectures. Our experiments demonstrate that the three modalities (text, emoji and images) encode different information to express emotion and therefore can complement each other. Our results also demonstrate that emoji sense depends on the textual context, and emoji combined with text encodes better information than considered separately. The highest accuracy of 71.98% is achieved with a training data of 550k posts. |
| Tasks | Emotion Classification |
| Published | 2019-03-13 |
| URL | http://arxiv.org/abs/1903.12520v1 |
| http://arxiv.org/pdf/1903.12520v1.pdf | |
| PWC | https://paperswithcode.com/paper/multimodal-emotion-classification |
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Spatio-Temporal Deep Graph Infomax
| Title | Spatio-Temporal Deep Graph Infomax |
| Authors | Felix L. Opolka, Aaron Solomon, Cătălina Cangea, Petar Veličković, Pietro Liò, R Devon Hjelm |
| Abstract | Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes over time. To address these issues, we introduce Spatio-Temporal Deep Graph Infomax (STDGI)—a fully unsupervised node representation learning approach based on mutual information maximization that exploits both the temporal and spatial dynamics of the graph. Our model tackles the challenging task of node-level regression by training embeddings to maximize the mutual information between patches of the graph, at any given time step, and between features of the central nodes of patches, in the future. We demonstrate through experiments and qualitative studies that the learned representations can successfully encode relevant information about the input graph and improve the predictive performance of spatio-temporal auto-regressive forecasting models. |
| Tasks | Representation Learning |
| Published | 2019-04-12 |
| URL | http://arxiv.org/abs/1904.06316v1 |
| http://arxiv.org/pdf/1904.06316v1.pdf | |
| PWC | https://paperswithcode.com/paper/spatio-temporal-deep-graph-infomax |
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Classification of glomerular hypercellularity using convolutional features and support vector machine
| Title | Classification of glomerular hypercellularity using convolutional features and support vector machine |
| Authors | Paulo Chagas, Luiz Souza, Ikaro Araújo, Nayze Aldeman, Angelo Duarte, Michele Angelo, Washington LC dos-Santos, Luciano Oliveira |
| Abstract | Glomeruli are histological structures of the kidney cortex formed by interwoven blood capillaries, and are responsible for blood filtration. Glomerular lesions impair kidney filtration capability, leading to protein loss and metabolic waste retention. An example of lesion is the glomerular hypercellularity, which is characterized by an increase in the number of cell nuclei in different areas of the glomeruli. Glomerular hypercellularity is a frequent lesion present in different kidney diseases. Automatic detection of glomerular hypercellularity would accelerate the screening of scanned histological slides for the lesion, enhancing clinical diagnosis. Having this in mind, we propose a new approach for classification of hypercellularity in human kidney images. Our proposed method introduces a novel architecture of a convolutional neural network (CNN) along with a support vector machine, achieving near perfect average results with the FIOCRUZ data set in a binary classification (lesion or normal). Our deep-based classifier outperformed the state-of-the-art results on the same data set. Additionally, classification of hypercellularity sub-lesions was also performed, considering mesangial, endocapilar and both lesions; in this multi-classification task, our proposed method just failed in 4% of the cases. To the best of our knowledge, this is the first study on deep learning over a data set of glomerular hypercellularity images of human kidney. |
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| Published | 2019-06-28 |
| URL | https://arxiv.org/abs/1907.00028v1 |
| https://arxiv.org/pdf/1907.00028v1.pdf | |
| PWC | https://paperswithcode.com/paper/classification-of-glomerular-hypercellularity |
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Deep Patent Landscaping Model Using Transformer and Graph Embedding
| Title | Deep Patent Landscaping Model Using Transformer and Graph Embedding |
| Authors | Seokkyu Choi, Hyeonju Lee, Eunjeong Lucy Park, Sungchul Choi |
| Abstract | Patent landscaping is a method used for searching related patents during a research and development (R&D) project. To avoid the risk of patent infringement and to follow current trends in technology, patent landscaping is a crucial task required during the early stages of an R&D project. As the process of patent landscaping requires advanced resources and can be tedious, the demand for automated patent landscaping has been gradually increasing. However, a shortage of well-defined benchmark datasets and comparable models makes it difficult to find related research studies. In this paper, we propose an automated patent landscaping model based on deep learning. To analyze the text of patents, the proposed model uses a modified transformer structure. To analyze the metadata of patents, we propose a graph embedding method that uses a diffusion graph called Diff2Vec. Furthermore, we introduce four benchmark datasets for comparing related research studies in patent landscaping. The datasets are produced by querying Google BigQuery, based on a search formula from a Korean patent attorney. The obtained results indicate that the proposed model and datasets can attain state-of-the-art performance, as compared with current patent landscaping models. |
| Tasks | Graph Embedding |
| Published | 2019-03-14 |
| URL | https://arxiv.org/abs/1903.05823v4 |
| https://arxiv.org/pdf/1903.05823v4.pdf | |
| PWC | https://paperswithcode.com/paper/a-deep-patent-landscaping-model-using |
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Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System
| Title | Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System |
| Authors | Marco Avvenuti, Salvatore Bellomo, Stefano Cresci, Leonardo Nizzoli, Maurizio Tesconi |
| Abstract | People involved in mass emergencies increasingly publish information-rich contents in online social networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work, we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid crowdsensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to 7x) and the variety (up to 18x) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity. |
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| Published | 2019-12-04 |
| URL | https://arxiv.org/abs/1912.02182v1 |
| https://arxiv.org/pdf/1912.02182v1.pdf | |
| PWC | https://paperswithcode.com/paper/towards-better-social-crisis-data-with-hermes |
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Support Vector Regression via a Combined Reward Cum Penalty Loss Function
| Title | Support Vector Regression via a Combined Reward Cum Penalty Loss Function |
| Authors | Pritam Anand, Reshma Rastogi, Suresh Chandra |
| Abstract | In this paper, we introduce a novel combined reward cum penalty loss function to handle the regression problem. The proposed combined reward cum penalty loss function penalizes the data points which lie outside the $\epsilon$-tube of the regressor and also assigns reward for the data points which lie inside of the $\epsilon$-tube of the regressor. The combined reward cum penalty loss function based regression (RP-$\epsilon$-SVR) model has several interesting properties which are investigated in this paper and are also supported with the experimental results. |
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| Published | 2019-04-28 |
| URL | http://arxiv.org/abs/1904.12331v1 |
| http://arxiv.org/pdf/1904.12331v1.pdf | |
| PWC | https://paperswithcode.com/paper/support-vector-regression-via-a-combined |
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SoulMate: Short-text author linking through Multi-aspect temporal-textual embedding
| Title | SoulMate: Short-text author linking through Multi-aspect temporal-textual embedding |
| Authors | Saeed Najafipour, Saeid Hosseini, Wen Hua, Mohammad Reza Kangavari, Xiaofang Zhou |
| Abstract | Linking authors of short-text contents has important usages in many applications, including Named Entity Recognition (NER) and human community detection. However, certain challenges lie ahead. Firstly, the input short-text contents are noisy, ambiguous, and do not follow the grammatical rules. Secondly, traditional text mining methods fail to effectively extract concepts through words and phrases. Thirdly, the textual contents are temporally skewed, which can affect the semantic understanding by multiple time facets. Finally, using the complementary knowledge-bases makes the results biased to the content of the external database and deviates the understanding and interpretation away from the real nature of the given short text corpus. To overcome these challenges, we devise a neural network-based temporal-textual framework that generates the tightly connected author subgraphs from microblog short-text contents. Our approach, on the one hand, computes the relevance score (edge weight) between the authors through considering a portmanteau of contents and concepts, and on the other hand, employs a stack-wise graph cutting algorithm to extract the communities of the related authors. Experimental results show that compared to other knowledge-centered competitors, our multi-aspect vector space model can achieve a higher performance in linking short-text authors. Additionally, given the author linking task, the more comprehensive the dataset is, the higher the significance of the extracted concepts will be. |
| Tasks | Community Detection, Named Entity Recognition |
| Published | 2019-10-27 |
| URL | https://arxiv.org/abs/1910.12180v1 |
| https://arxiv.org/pdf/1910.12180v1.pdf | |
| PWC | https://paperswithcode.com/paper/soulmate-short-text-author-linking-through |
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Hierarchical Clustering for Smart Meter Electricity Loads based on Quantile Autocovariances
| Title | Hierarchical Clustering for Smart Meter Electricity Loads based on Quantile Autocovariances |
| Authors | Andrés M. Alonso, F. Javier Nogales, Carlos Ruiz |
| Abstract | In order to improve the efficiency and sustainability of electricity systems, most countries worldwide are deploying advanced metering infrastructures, and in particular household smart meters, in the residential sector. This technology is able to record electricity load time series at a very high frequency rates, information that can be exploited to develop new clustering models to group individual households by similar consumptions patterns. To this end, in this work we propose three hierarchical clustering methodologies that allow capturing different characteristics of the time series. These are based on a set of “dissimilarity” measures computed over different features: quantile auto-covariances, and simple and partial autocorrelations. The main advantage is that they allow summarizing each time series in a few representative features so that they are computationally efficient, robust against outliers, easy to automatize, and scalable to hundreds of thousands of smart meters series. We evaluate the performance of each clustering model in a real-world smart meter dataset with thousands of half-hourly time series. The results show how the obtained clusters identify relevant consumption behaviors of households and capture part of their geo-demographic segmentation. Moreover, we apply a supervised classification procedure to explore which features are more relevant to define each cluster. |
| Tasks | Time Series |
| Published | 2019-11-08 |
| URL | https://arxiv.org/abs/1911.03336v1 |
| https://arxiv.org/pdf/1911.03336v1.pdf | |
| PWC | https://paperswithcode.com/paper/hierarchical-clustering-for-smart-meter |
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Interval Valued Trapezoidal Neutrosophic Set for Prioritization of Non-functional Requirements
| Title | Interval Valued Trapezoidal Neutrosophic Set for Prioritization of Non-functional Requirements |
| Authors | Kiran Khatter |
| Abstract | This paper discusses the trapezoidal fuzzy number(TrFN); Interval-valued intuitionistic fuzzy number(IVIFN); neutrosophic set and its operational laws; and, trapezoidal neutrosophic set(TrNS) and its operational laws. Based on the combination of IVIFN and TrNS, an Interval Valued Trapezoidal Neutrosophic Set (IVTrNS) is proposed followed by its operational laws. The paper also presents the score and accuracy functions for the proposed Interval Valued Trapezoidal Neutrosophic Number (IVTrNN). Then, an interval valued trapezoidal neutrosophic weighted arithmetic averaging (IVTrNWAA) operator is introduced to combine the trapezoidal information which is neutrosophic and in the unit interval of real numbers. Finally, a method is developed to handle the problems in the multi attribute decision making(MADM) environment using IVTrNWAA operator followed by a numerical example of NFRs prioritization to illustrate the relevance of the developed method. |
| Tasks | Decision Making |
| Published | 2019-05-07 |
| URL | https://arxiv.org/abs/1905.05238v1 |
| https://arxiv.org/pdf/1905.05238v1.pdf | |
| PWC | https://paperswithcode.com/paper/190505238 |
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Predicting Heart Failure Readmission from Clinical Notes Using Deep Learning
| Title | Predicting Heart Failure Readmission from Clinical Notes Using Deep Learning |
| Authors | Xiong Liu, Yu Chen, Jay Bae, Hu Li, Joseph Johnston, Todd Sanger |
| Abstract | Heart failure hospitalization is a severe burden on healthcare. How to predict and therefore prevent readmission has been a significant challenge in outcomes research. To address this, we propose a deep learning approach to predict readmission from clinical notes. Unlike conventional methods that use structured data for prediction, we leverage the unstructured clinical notes to train deep learning models based on convolutional neural networks (CNN). We then use the trained models to classify and predict potentially high-risk admissions/patients. For evaluation, we trained CNNs using the discharge summary notes in the MIMIC III database. We also trained regular machine learning models based on random forest using the same datasets. The result shows that deep learning models outperform the regular models in prediction tasks. CNN method achieves a F1 score of 0.756 in general readmission prediction and 0.733 in 30-day readmission prediction, while random forest only achieves a F1 score of 0.674 and 0.656 respectively. We also propose a chi-square test based method to interpret key features associated with deep learning predicted readmissions. It reveals clinical insights about readmission embedded in the clinical notes. Collectively, our method can make the human evaluation process more efficient and potentially facilitate the reduction of readmission rates. |
| Tasks | Readmission Prediction |
| Published | 2019-12-21 |
| URL | https://arxiv.org/abs/1912.10306v1 |
| https://arxiv.org/pdf/1912.10306v1.pdf | |
| PWC | https://paperswithcode.com/paper/predicting-heart-failure-readmission-from |
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A Variational Approach for Learning from Positive and Unlabeled Data
| Title | A Variational Approach for Learning from Positive and Unlabeled Data |
| Authors | Hui Chen, Fangqing Liu, Yin Wang, Liyue Zhao, Hao Wu |
| Abstract | Learning binary classifiers only from positive and unlabeled (PU) data is an important and challenging task in many real-world applications, including web text classification, disease gene identification and fraud detection, where negative samples are difficult to verify experimentally. Most recent PU learning methods are developed based on the conventional misclassification risk of the supervised learning type, and they require to solve the intractable risk estimation problem by approximating the negative data distribution or the class prior. In this paper, we introduce a variational principle for PU learning that allows us to quantitatively evaluate the modeling error of the Bayesian classifier directly from given data. This leads to a loss function which can be efficiently calculated without any intermediate step or model, and a variational learning method can then be employed to optimize the classifier under general conditions. In addition, the discriminative performance and numerical stability of the variational PU learning method can be further improved by incorporating a margin maximizing loss function. We illustrate the effectiveness of the proposed variational method on a number of benchmark examples. |
| Tasks | Fraud Detection, Text Classification |
| Published | 2019-06-03 |
| URL | https://arxiv.org/abs/1906.00642v4 |
| https://arxiv.org/pdf/1906.00642v4.pdf | |
| PWC | https://paperswithcode.com/paper/190600642 |
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AutoDiscern: Rating the Quality of Online Health Information with Hierarchical Encoder Attention-based Neural Networks
| Title | AutoDiscern: Rating the Quality of Online Health Information with Hierarchical Encoder Attention-based Neural Networks |
| Authors | Laura Kinkead, Ahmed Allam, Michael Krauthammer |
| Abstract | Patients increasingly turn to search engines and online content before, or in place of, talking with a health professional. Low quality health information, which is common on the internet, presents risks to the patient in the form of misinformation and a possibly poorer relationship with their physician. To address this, the DISCERN criteria (developed at University of Oxford) are used to evaluate the quality of online health information. However, patients are unlikely to take the time to apply these criteria to the health websites they visit. We built an automated implementation of the DISCERN instrument (Brief version) using machine learning models. We compared the performance of a traditional model (Random Forest) with that of a hierarchical encoder attention-based neural network (HEA) model using two language embeddings, BERT and BioBERT. The HEA BERT and BioBERT models achieved average F1-macro scores across all criteria of 0.75 and 0.74, respectively, outperforming the Random Forest model (average F1-macro = 0.69). Similarly, as measured by F-micro, HEA BERT and BioBERT scored on average 0.80 and 0.81 vs. 0.76 for the Random Forest model. Overall, the neural network based models achieved 81% and 86% average accuracy at 100% and 80% coverage, respectively, compared to 94% manual rating accuracy. The attention mechanism implemented in the HEA architectures provided ‘model explainability’ by identifying reasonable supporting sentences for the documents fulfilling the Brief DISCERN criteria. Our research suggests that it is feasible to automate online health information quality assessment, which is an important step towards empowering patients to become informed partners in the healthcare process. |
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| Published | 2019-12-30 |
| URL | https://arxiv.org/abs/1912.12999v2 |
| https://arxiv.org/pdf/1912.12999v2.pdf | |
| PWC | https://paperswithcode.com/paper/autodiscern-rating-the-quality-of-online |
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Generating Multiple Diverse Responses with Multi-Mapping and Posterior Mapping Selection
| Title | Generating Multiple Diverse Responses with Multi-Mapping and Posterior Mapping Selection |
| Authors | Chaotao Chen, Jinhua Peng, Fan Wang, Jun Xu, Hua Wu |
| Abstract | In human conversation an input post is open to multiple potential responses, which is typically regarded as a one-to-many problem. Promising approaches mainly incorporate multiple latent mechanisms to build the one-to-many relationship. However, without accurate selection of the latent mechanism corresponding to the target response during training, these methods suffer from a rough optimization of latent mechanisms. In this paper, we propose a multi-mapping mechanism to better capture the one-to-many relationship, where multiple mapping modules are employed as latent mechanisms to model the semantic mappings from an input post to its diverse responses. For accurate optimization of latent mechanisms, a posterior mapping selection module is designed to select the corresponding mapping module according to the target response for further optimization. We also introduce an auxiliary matching loss to facilitate the optimization of posterior mapping selection. Empirical results demonstrate the superiority of our model in generating multiple diverse and informative responses over the state-of-the-art methods. |
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| Published | 2019-06-05 |
| URL | https://arxiv.org/abs/1906.01781v1 |
| https://arxiv.org/pdf/1906.01781v1.pdf | |
| PWC | https://paperswithcode.com/paper/generating-multiple-diverse-responses-with |
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Online Planning for Decentralized Stochastic Control with Partial History Sharing
| Title | Online Planning for Decentralized Stochastic Control with Partial History Sharing |
| Authors | Kaiqing Zhang, Erik Miehling, Tamer Başar |
| Abstract | In decentralized stochastic control, standard approaches for sequential decision-making, e.g. dynamic programming, quickly become intractable due to the need to maintain a complex information state. Computational challenges are further compounded if agents do not possess complete model knowledge. In this paper, we take advantage of the fact that in many problems agents share some common information, or history, termed partial history sharing. Under this information structure the policy search space is greatly reduced. We propose a provably convergent, online tree-search based algorithm that does not require a closed-form model or explicit communication among agents. Interestingly, our algorithm can be viewed as a generalization of several existing heuristic solvers for decentralized partially observable Markov decision processes. To demonstrate the applicability of the model, we propose a novel collaborative intrusion response model, where multiple agents (defenders) possessing asymmetric information aim to collaboratively defend a computer network. Numerical results demonstrate the performance of our algorithm. |
| Tasks | Decision Making |
| Published | 2019-08-06 |
| URL | https://arxiv.org/abs/1908.02357v1 |
| https://arxiv.org/pdf/1908.02357v1.pdf | |
| PWC | https://paperswithcode.com/paper/online-planning-for-decentralized-stochastic |
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