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arXiv 2019年

作者： Piette, Cedric Piette, Eric Stephenson, Matthew Soemers, Dennis J.N.J. Browne, Cameron Centre de Recherche en Informatique de Lens Universite d'Artois Lens France Department of Data Science and Knowledge Engineering Maastricht University Maastricht

Many of the famous single-player games, commonly called puzzles, can be shown to be NP-Complete. Indeed, this class of complexity contains hundreds of puzzles, since people particularly appreciate completing an intractable puzzle, such as Sudoku, but also enjoy the ability to check their solution easily once it's done. For this reason, using constraint programming is naturally suited to solve them. In this paper, we focus on logic puzzles described in the Ludii general game system and we propose using the XCSP formalism in order to solve them with any CSP solver. Copyright © 2019, The Authors. All rights reserved.

关键词： knowledge representation

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Integrating explainable machine learning and transcriptomics data reveals cell-type specific immune signatures underlying macular degeneration

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NPJ genomic medicine 2025年第1期10卷 48页

作者： Khang Ma Hosei Nakajima Nipa Basak Arko Barman Rinki Ratnapriya Department of Ophthalmology Baylor College of Medicine Houston TX USA. Data to Knowledge Lab Rice University Houston TX USA. Department of Electrical & Computer Engineering Rice University Houston TX USA. Department of Statistics Rice University Houston TX USA. Department of Ophthalmology Baylor College of Medicine Houston TX USA. rpriya@bcm.edu. Department of Biochemistry and Molecular Pharmacology Baylor College of Medicine Houston TX USA. rpriya@bcm.edu.

Genome-wide association studies (GWAS) have established key role of immune dysfunction in Age-related Macular Degeneration (AMD), though the precise role of immune cells remains unclear. Here, we develop an explainable machine-learning pipeline (ML) using transcriptome data of 453 donor retinas, identifying 81 genes distinguishing AMD from controls (AUC-ROC of 0.80, CI 0.70-0.92). Most of these genes were enriched in their expression within retinal glial cells, particularly microglia and astrocytes. Their role in AMD was further strengthened by cellular deconvolution, which identified distinct differences in microglia and astrocytes between normal and AMD. We corroborated these findings using independent single-cell data, where several ML genes exhibited differential expression. Finally, the integration of AMD-GWAS data identified a regulatory variant, rs4133124 at PLCG2, as a novel AMD association. Collectively, our study provides molecular insights into the recurring theme of immune dysfunction in AMD and highlights the significance of glial cell differences in AMD progression.

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Endurable SSD-Based Read Cache for Improving the Performance of Selective Restore from Deduplication Systems

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Journal of Computer Science & Technology 2018年第1期33卷 58-78页

作者： Jian Liu Yun-Peng Chai Xiao Qin Yao-Hong Liu Division of Computer Science and Engineering Louisiana State University Baton Rouge LA 70803 U.S.A. Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Shelby Center for Engineering Technology Department of Computer Science and Software Engineering Samuel Ginn College of Engineering Auburn University Auburn AL 368~9-53~7 U.S.A.

Deduplication has been commonly used in both enterprise storage systems and cloud storage. To overcome the performance challenge for the selective restore operations of deduplication systems, solid-state-drive-based （i.e., SSD-based） re^d cache cm, be deployed for speeding up by caching popular restore contents dynamically. Unfortunately, frequent data updates induced by classical cache schemes （e.g., LRU and LFU） significantly shorten SSDs＇ lifetime while slowing down I/O processes in SSDs. To address this problem, we propose a new solution -- LOP-Cache to greatly improve tile write durability of SSDs as well as I/O performance by enlarging the proportion of long-term popular （LOP） data among data written into SSD-based cache. LOP-Cache keeps LOP data in the SSD cache for a long time period to decrease the number of cache replacements. Furthermore, it prevents unpopular or unnecessary data in deduplication containers from being written into the SSD cache. We implemented LOP-Cache in a prototype deduplication system to evaluate its pertbrmance. Our experimental results indicate that LOP-Cache shortens the latency of selective restore by an average of 37.3% at the cost of a small SSD-based cache with only 5.56% capacity of the deduplicated data. Importantly, LOP-Cache improves SSDs＇ lifetime by a factor of 9.77. The evidence shows that LOP-Cache offers a cost-efficient SSD-based read cache solution to boost performance of selective restore for deduplication systems.

关键词： data deduplication solid state drive （SSD） flash cache endurance

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On Training Classifiers for Linking Event Templates 27

On Training Classifiers for Linking Event Templates

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2018 Events and Stories in the News Workshop, EventStory 2018, colocated with the 27th International Conference on Computational Linguistics, COLING 2018

作者： Piskorski, Jakub Šarić, Fredi Zavarella, Vanni Atkinson, Martin Text and Data Mining Unit Joint Research Centre of the European Ispra Italy Text Analysis and Knowledge Engineering Lab University of Zagreb Croatia

ISBN: (纸本)9781948087599

The paper reports on exploring various machine learning techniques and a range of textual and meta-data features to train classifiers for linking related event templates automatically extracted from online news. With the best model using textual features only we achieved 94.7% (92.9%) F1 score on GOLD (SILVER) dataset. These figures were further improved to 98.6% (GOLD) and 97% (SILVER) F1 score by adding meta-data features, mainly thanks to the strong discriminatory power of automatically extracted geographical information related to events. © 2018 EventStory 2018 - Events and Stories in the News, Proceedings of the Workshop. All rights reserved.

关键词： Gold

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Exploring Social Media data for MOOC Recommendation

Exploring Social Media Data for MOOC Recommendation

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International Conference on Systems of Collaboration (SysCo)

作者： Sara Assami Najima Daoudi Rachida Ajhoun Department of Computer Science Smart Systems Laboratory Mohammed Vth University Rabat Morocco Department of Data and Knowledge Engineering Information Sciences School (ESI) Rabat Morocco

ISBN: (数字)9781728142166

ISBN: (纸本)9781728142173

MOOCs (Massive Open Online Courses) are a key tool for open education stakeholders and high-level universities to face the problem of limited resources and make education accessible worldwide. Our study relies on the hypothesis of the influence of learning adaptation on the learners' lack of motivation in MOOCs. In this sense, a recommendation approach could stimulate the learner's interest in specific MOOCs. In previous research, we have identified the recommendation criteria to recommend MOOCs adapted to the learner's needs and motivations. Then, we modeled the ontology of the learning actors' profiles for the matching of the characteristics of learners with adapted MOOCs. Afterward, we designed the functional architecture and use cases of a semantic recommendation system to stimulate learners' interest in MOOCs. However, the recommendation process is still hindered by the learner profile on these platforms that doesn't represent the learner's interests and motivations. Yet, social media profiles extraction can be an external data source for learners' profile enrichment. So, the purpose of this research is to use social media mining for the acquisition of personal and professional data about learners. Therefore, we apply the first phases of data mining on a social media dataset to explore the potentially meaningful data for MOOC recommendation.

关键词： Social networking (online) data mining Educational courses Recommender systems Computer aided instruction

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Myocardial Ischemia Diagnosis Using a Reduced Lead System 40

Myocardial Ischemia Diagnosis Using a Reduced Lead System

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40th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2018

作者： Aranda Hernandez, A. Bonizzi, P. Karel, J. Peeters, R. Department of Research and Technology Bakken Research Center Medtronic Maastricht Netherlands Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9781538636466

This research presents a novel statistical model for diagnosing acute myocardial infarction (AMI). The model is based on features extracted from a reduced lead system consisting of a subset of three leads from the standard 12-lead ECG. We selected a set of relevant parameters commonly used in the clinical practice for ECG-based AMI diagnosis, namely ST elevation and T-wave maximum. We also selectedfeatures, not used in clinical practice, that were derived from vectorcardiography and computed on the reduced three-lead system (pseudo-VCG parameters). To validate the model, we used 104 patients coming from the Physionet STAFF III database which contains 12-lead ECG recordings at baseline and in coronary artery occlusion condition during angioplasty (PTCA). Results show that pseudo-VCG features are able to diagnose AMI slightly better than ST elevation and T-wave maximum features together (area under the ROC curve (AUC) 0.87 vs AUC 0.85). When combining pseudo-VCG features together with ST elevation, and T-wave maximum, the performance improved significantly (AUC 0.95, sensitivity 89.6% and specificity 82.7%). Results indicate a potential for diagnosing AMI using the proposed reduced lead system and the selected set of features. We suggest its possible use for diagnosing AMI in long-term, ambulatory and home monitoring situations, allowing an earlier and faster diagnosis. © 2018 IEEE.

关键词： Electrocardiography Feature extraction Heart Myocardium databases Lead Standards

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ALOR: Adaptive layout optimization of raft groups for heterogeneous distributed key-value stores 1

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15th IFIP International Conference on Network and Parallel Computing, NPC 2018

作者： Wang, Yangyang Chai, Yunpeng Wang, Xin Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China School of Information Renmin University of China Beijing China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9783030056773

ISBN: (纸本)9783030056766

Many distributed key-value storage systems employ the simple and effective Raft protocol to ensure data consistency. They usually assume a homogeneous node hardware configuration for the underlying cluster and thus adopt even data distribution schemes. However, today’s distributed systems tend to be heterogeneous in nodes’ I/O devices due to the regular worn I/O device replacement and the emergence of expensive new storage media (e.g., non-volatile memory). In this paper, we propose a new data layout scheme called Adaptive Layout Optimization of Raft groups (ALOR), considering the hardware heterogeneity of the cluster. ALOR aims to optimize the data layout of Raft groups to achieve a better practical load balance, which leads to higher performance. ALOR consists of two components: leader migration in Raft groups and skewed data layout based on cold data migration. We conducted experiments on a practical heterogeneous cluster, and the results indicate that, on average, ALOR improves throughput by 36.89%, reduces latency and 99th percentile tail latency by 24.54% and 21.32%, respectively. © IFIP International Federation for Information Processing 2018.

关键词： Digital storage

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SMILE-UHURA Challenge - Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms

arXiv

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arXiv 2024年

作者： Chatterjee, Soumick Mattern, Hendrik Dörner, Marc Sciarra, Alessandro Dubost, Florian Schnurre, Hannes Khatun, Rupali Yu, Chun-Chih Hsieh, Tsung-Lin Tsai, Yi-Shan Fang, Yi-Zeng Yang, Yung-Ching Huang, Juinn-Dar Xu, Marshall Liu, Siyu Ribeiro, Fernanda L. Bollmann, Saskia Chintalapati, Karthikesh Varma Radhakrishna, Chethan Mysuru Ram Kumar, Sri Chandana Hudukula Sutrave, Raviteja Qayyum, Abdul Mazher, Moona Razzak, Imran Rodero, Cristobal Niederer, Steven Lin, Fengming Xia, Yan Wang, Jiacheng Qiu, Riyu Wang, Liansheng Panah, Arya Yazdan Jurdi, Rosana El Fu, Guanghui Arslan, Janan Vaillant, Ghislain Valabregue, Romain Dormont, Didier Stankoff, Bruno Colliot, Olivier Vargas, Luisa Chacón, Isai Daniel Pitsiorlas, Ioannis Arbeláez, Pablo Zuluaga, Maria A. Schreiber, Stefanie Speck, Oliver Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Magdeburg Germany Department of Neurology Medical Faculty University Hospital of Magdeburg Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany University Hospital Zurich University of Zurich Zurich Switzerland Google Inc. United States Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexnder-Universität Erlangen-Nürnberg Erlangen Germany National Yang Ming Chiao Tung University Hsinchu Taiwan School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia Australian eHealth Research Centre Chile National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Computer Science and Engineering University of New South Wales Sydney Australia Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates The Alan Turing Institute London United Kingdom School of Computing University of Leeds Leeds United Kingdom Department of Computer Science School of Informatics Xiamen University Xiamen China Manteia Technologies Co. Ltd Xiamen China Leicester International Institute Dalian University of Technology Dalian China Sorbonne Université Institut du Cerveau - Paris Brain Institute ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié Salpêtrière ParisF-75013 France Centre of Formation and Research in Artificial Intelligence Universidad de Los Andes Colombia Data Science Department EURECOM Sophia Antipolis Franc

The human brain receives nutrients and oxygen through an intricate network of blood vessels. Pathology affecting small vessels, at the mesoscopic scale, represents a critical vulnerability within the cerebral blood supply and can lead to severe conditions, such as Cerebral Small Vessel Diseases. The advent of 7 Tesla MRI systems has enabled the acquisition of higher spatial resolution images, making it possible to visualise such vessels in the brain. However, the lack of publicly available annotated datasets has impeded the development of robust, machine learning-driven segmentation algorithms. To address the complexities of mesoscopic vessel segmentation and to highlight the need for advanced techniques to manage the high noise levels and poor vessel-to-background contrast inherent in"ultra-high-resolution" data, the SMILE-UHURA challenge was organised. This challenge, held in conjunction with the ISBI 2023, in Cartagena de Indias, Colombia, aimed to provide a platform for researchers working on related topics. The SMILE-UHURA challenge addresses the gap in publicly available annotated datasets by providing an annotated dataset of Time-of-Flight angiography acquired with 7T MRI. This dataset was created through a combination of automated pre-segmentation and extensive manual refinement. In this manuscript, sixteen submitted methods and two baseline methods are compared both quantitatively and qualitatively on two different datasets: held-out test MRAs from the same dataset as the training data (with labels kept secret) and a separate 7T ToF MRA dataset where both input volumes and labels are kept secret. The results demonstrate that most of the submitted deep learning methods, trained on the provided training dataset, achieved reliable segmentation performance. Dice scores reached up to 0.838 ± 0.066 and 0.716 ± 0.125 on the respective datasets, with an average performance of up to 0.804 ± 0.15. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization

arXiv

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arXiv 2020年

作者： Wang, Weisen Li, Xirong Xu, Zhiyan Yu, Weihong Zhao, Jianchun Ding, Dayong Chen, Youxin The MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing100872 China Aimc Lab School of Information Renmin University of China Beijing100872 China Vistel Ai Lab Visionary Intelligence Ltd. Beijing100872 China The Key Lab of Ocular Fundus Disease Chinese Academy of Medical Sciences Dept. of Ophthalmology Peking Union Medical College Hospital Beijing100730 China

This paper tackles automated categorization of Agerelated Macular Degeneration (AMD), a common macular disease among people over 50. Previous research efforts mainly focus on AMD categorization with a single-modal input, let it be a color fundus photograph (CFP) or an OCT B-scan image. By contrast, we consider AMD categorization given a multi-modal input, a direction that is clinically meaningful yet mostly unexplored. Contrary to the prior art that takes a traditional approach of feature extraction plus classifier training that cannot be jointly optimized, we opt for end-to-end multi-modal Convolutional Neural Networks (MM-CNN). Our MM-CNN is instantiated by a two-stream CNN, with spatially-invariant fusion to combine information from the CFP and OCT streams. In order to visually interpret the contribution of the individual modalities to the final prediction, we extend the class activation mapping (CAM) technique to the multi-modal scenario. For effective training of MM-CNN, we develop two data augmentation methods. One is GAN-based CFP/OCT image synthesis, with our novel use of CAMs as conditional input of a high-resolution image-to-image translation GAN. The other method is Loose Pairing, which pairs a CFP image and an OCT image on the basis of their classes instead of eye identities. Experiments on a clinical dataset consisting of 1,094 CFP images and 1,289 OCT images acquired from 1,093 distinct eyes show that the proposed solution obtains better F1 and Accuracy than multiple baselines for multimodal AMD categorization. Code and data are available at https://***/li-xirong/mmc-amd. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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MaaSim: A liveability simulation for improving the quality of life in cities

arXiv

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arXiv 2018年

作者： Woszczyk, Dominika Spanakis, Gerasimos Department of Data Science and Knowledge Engineering Maastricht University

Urbanism is no longer planned on paper thanks to powerful models and 3D simulation platforms. However, current work is not open to the public and lacks an optimisation agent that could help in decision making. This paper describes the creation of an open-source simulation based on an existing Dutch liveability score with a built-in AI module. Features are selected using feature engineering and Random Forests. Then, a modified scoring function is built based on the former liveability classes. The score is predicted using Random Forest for regression and achieved a recall of 0.83 with 10-fold cross-validation. Afterwards, Exploratory Factor Analysis is applied to select the actions present in the model. The resulting indicators are divided into 5 groups, and 12 actions are generated. The performance of four optimisation algorithms is compared, namely NSGA-II, PAES, SPEA2 and Ε-MOEA, on three established criteria of quality: cardinality, the spread of the solutions, spacing, and the resulting score and number of turns. Although all four algorithms show different strengths, Ε−MOEA is selected to be the most suitable for this problem. Ultimately, the simulation incorporates the model and the selected AI module in a GUI written in the Kivy framework for Python. Tests performed on users show positive responses and encourage further initiatives towards joining technology and public applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Multiobjective optimization

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