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TelescopeML - I. An End-to-End Python Package for Interpreting Telescope Datasets through Training Machine Learning Models, Generating Statistical Reports, and Visualizing Results

arXiv

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arXiv 2024年

作者： Gharib-Nezhad, Ehsan Batalha, Natasha E. Valizadegan, Hamed Martinho, Miguel J.S. Habibi, Mahdi Nookula, Gopal Space Science and Astrobiology Division NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Research Park Moffett Field CA94035 United States Mountain ViewCA94043 United States Intelligent Systems Division NASA Ames Research Center Moffett Field CA94035 United States Institute for Radiation Physics Helmholtz-Zentrum Dresden-Rossendorf Dresden01328 Germany Department of Computer Science University of California Riverside RiversideCA92507 United States

We are on the verge of a revolutionary era in space exploration, thanks to advancements in telescopes such as the James Webb Space Telescope (JWST). High-resolution, high signal-to-noise spectra from exoplanet and brown dwarf atmospheres have been collected over the past few decades, requiring the development of accurate and reliable pipelines and tools for their analysis. Accurately and swiftly determining the spectroscopic parameters from the observational spectra of these objects is crucial for understanding their atmospheric composition and guiding future follow-up observations. TelescopeML is a Python package developed to perform three main tasks: 1. Process the synthetic astronomical datasets for training a CNN model and prepare the observational dataset for later use for prediction;2. Train a CNN model by implementing the optimal hyperparameters;and 3. Deploy the trained CNN models on the actual observational data to derive the output spectroscopic parameters. The implications and scientific outcomes from the trained CNN models and this package are under revision for The Astrophysical Journal under the title TelescopeML – II: Convolutional Neural Networks for Predicting Brown Dwarf Atmospheric Parameters. © 2024, CC BY.

关键词： Python

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Continual relation learning via episodic memory activation and reconsolidation 58

Continual relation learning via episodic memory activation a...

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Han, Xu Dai, Yi Gao, Tianyu Lin, Yankai Liu, Zhiyuan Li, Peng Sun, Maosong Zhou, Jie State Key Lab on Intelligent Technology and Systems Institute for Artificial Intelligence Department of Computer Science and Technology Tsinghua University Beijing China Pattern Recognition Center WeChat AI Tencent Inc. China

ISBN: (纸本)9781952148255

Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations. Some pioneering work has proved that storing a handful of historical relation examples in episodic memory and replaying them in subsequent training is an effective solution for such a challenging problem. However, these memory-based methods usually suffer from overfitting the few memorized examples of old relations, which may gradually cause inevitable confusion among existing relations. Inspired by the mechanism in human long-term memory formation, we introduce episodic memory activation and reconsolidation (EMAR) to continual relation learning. Every time neural models are activated to learn both new and memorized data, EMAR utilizes relation prototypes for memory reconsolidation exercise to keep a stable understanding of old relations. The experimental results show that EMAR could get rid of catastrophically forgetting old relations and outperform the state-of-the-art continual learning models. The code and datasets are released on https://***/thunlp/ContinualRE. © 2020 Association for Computational Linguistics

关键词： Chemical activation

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Efficient Random Subspace Decision Forests with A Simple Probability Dimensionality Setting Scheme

SSRN

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SSRN 2022年

作者： Wang, Quan Wang, Fei Li, Zhongheng Jiang, Peilin Ren, Fuji Nie, Feiping Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing100094 China School of Software Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Northwestern Polytechnical University Shaanxi Xi’an710072 China

The random subspace concept is widely used in decision forests. However, there is not a reasonable approach to specify the appropriate number of randomly selected features. Previous random subspace decision forests simply specify the random subspace dimensionality d s to a preset function value such as [log 2 d ] or [√ d ] where d is the number of the total feature dimensions. Actually, this preset value may be a relatively poor choice for high dimensional data sets. In this paper, a novel framework named Efficient Random Subspace decision forest (ERS) is proposed which pays attention to how to set the random subspace dimensionality for each decision tree of decision forests. Specifically, a simple discrete uniform distribution is employed to set with probability the number of randomly selected features for each tree in random subspace decision forests. The proposed Half Range Discrete Uniform distribution-based Varied Dimensionality setting (HRDUVD) method removes the hesitation of how to preset an appropriate d s for different data sets but also results in good enough classification performance along with relatively short running time. Plenty of experiments on public benchmark data sets have demonstrated the effectiveness and the efficiency of the proposed ERS. © 2022, The Authors. All rights reserved.

关键词： Decision trees

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The Msr-Video to Text Dataset with Clean Annotations

SSRN

引用

SSRN 2022年

作者： Hu, Xiaolin Chen, Haoran Li, Jianmin Frintrop, Simone State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Institute for Artificial Intelligence Tsinghua University Beijing100084 China Beijing100010 China Department of Informatics University of Hamburg Hamburg20148 Germany

Video captioning automatically generates short descriptions of the video content, usually in form of a single sentence. Many methods have been proposed for solving this task. A large dataset called MSR Video to Text (MSR-VTT) is often used as the benchmark dataset for testing the performance of the methods. However, we found that the human annotations, i.e., the descriptions of video contents in the dataset are quite noisy, e.g., there are many duplicate captions and many captions contain grammatical problems. These problems may pose difficulties to video captioning models for learning underlying patterns. We cleaned the MSR-VTT annotations by removing these problems, then tested several typical video captioning models on the cleaned dataset. Experimental results showed that data cleaning boosted the performances of the models measured by popular quantitative metrics. We recruited subjects to evaluate the results of a model trained on the original and cleaned datasets. The human behavior experiment demonstrated that trained on the cleaned dataset, the model generated captions that were more coherent and more relevant to the contents of the video clips. © 2022, The Authors. All rights reserved.

关键词： Video recording

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Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning

arXiv

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arXiv 2024年

作者： Gao, Fengyi Zhang, Xingyu Sivarajkumar, Sonish Denny, Parker E. Aldhahwani, Bayan M. Visweswaran, Shyam Shi, Ryan Hogan, William Bove, Allyn Wang, Yanshan Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Communication Science and Disorders University of Pittsburgh PittsburghPA United States Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Physical Therapy University of Pittsburgh PittsburghPA United States Department of Medical Rehabilitation Sciences Umm Al-Qura University Makkah Saudi Arabia Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Department of Computer Science University of Pittsburgh Medical Center PittsburghPA United States Data Science Institute Medical College of Wisconsin MilwaukeeWI United States Hillman Cancer Center University of Pittsburgh Medical Center PittsburghPA United States

Objective In this study, we utilized statistical analysis and machine learning methods to examine whether rehabilitation exercises can improve patients post-stroke functional abilities, as well as forecast the improvement in functional abilities. Our dataset is patients’ rehabilitation exercises and demographic information recorded in the unstructured electronic health records (EHRs) data and free-text rehabilitation procedure notes. Through this study, our ultimate goal is to pinpoint the specific rehabilitation exercises that can effectively aid post-stroke patients in improving their functional outcomes in basic mobility (BM) and applied cognitive (AC) domains. Data sources We collected data for 265 stroke patients from the University of Pittsburgh Medical Center, accessed through the Rehabilitation Datamart With Informatics iNfrastructure for Research (ReDWINE). Methods We employed a pre-existing natural language processing (NLP) algorithm to extract data on rehabilitation exercises and developed a rule-based NLP algorithm to extract Activity Measure for Post-Acute Care (AM-PAC) scores, covering basic mobility (BM) and applied cognitive (AC) domains, from procedure notes. AM-PAC scores were collected at the initial rehabilitation visit and followed up at one and two months—key recovery periods. Changes in AM-PAC scores were classified based on the minimal clinically important difference (MCID), and significance was assessed using Friedman and Wilcoxon tests. To identify impactful exercises, we used Chi-square tests, Fisher's exact tests, and logistic regression for odds ratios. Additionally, we developed five machine learning models—logistic regression (LR), Adaboost (ADB), support vector machine (SVM), gradient boosting (GB), and random forest (RF)—to predict outcomes in functional ability. Results Statistical analyses revealed significant associations between functional improvements and specific exercises. In the AC domain, the BALANCE exercise showed substant

关键词： Statistical tests

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A model for efficient dynamical ranking in networks

arXiv

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arXiv 2023年

作者： Della Vecchia, Andrea Neocosmos, Kibidi Larremore, Daniel B. Moore, Cristopher De Bacco, Caterina Istituto Italiano di Tecnologia Genoa Italy MaLGa Center Università di Genova Genoa Italy Max Planck Institute for Intelligent Systems Tübingen Germany African Institute for Mathematical Sciences Cape Town South Africa University of Tübingen Tübingen Germany Department of Computer Science University of Colorado Boulder BoulderCO United States BioFrontiers Institute University of Colorado Boulder BoulderCO United States Santa Fe Institute Santa FeNM United States

We present a physics-inspired method for inferring dynamic rankings in directed temporal networks—networks in which each directed and timestamped edge reflects the outcome and timing of a pairwise interaction. The inferred ranking of each node is real-valued and varies in time as each new edge, encoding an outcome like a win or loss, raises or lowers the node’s estimated strength or prestige, as is often observed in real scenarios including sequences of games, tournaments, or interactions in animal hierarchies. Our method works by solving a linear system of equations and requires only one parameter to be tuned. As a result, the corresponding algorithm is scalable and efficient. We test our method by evaluating its ability to predict interactions (edges’ existence) and their outcomes (edges’ directions) in a variety of applications, including both synthetic and real data. Our analysis shows that in many cases our method’s performance is better than existing methods for predicting dynamic rankings and interaction outcomes. © 2023, CC BY-NC-SA.

关键词： Linear systems

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Communication Support Utilizing AAC for Verbally Challenged Children in Developing Countries During COVID-19 Pandemic 22nd

Communication Support Utilizing AAC for Verbally Challenged ...

引用

22nd International Conference on Human-computer Interaction, HCI International 2020

作者： Farzana, Walia Sarker, Farhana Vaidyanathan, Ravi Chau, Tom Mamun, Khondaker A. Advanced Intelligent Multidisciplinary Systems Lab Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Mechanical Engineering Imperial College London London United Kingdom Institute of Biomaterials and Biomedical Engineering University of Toronto Toronto Canada

ISBN: (纸本)9783030607029

Functional communication is indispensable for child development at all times but during this COVID-19, non-verbal children become more anxious about social distancing and self-quarantine due to sudden aberration on daily designed practices and professional support. These verbally challenged children require the support of Augmentative and Alternative Communication (AAC) for intercommunication. Therefore, during COVID-19, assistance must be provided remotely to these users by a AAC team involving caregivers, teachers, Speech Language Therapist (SLT) to ensure collaborative learning and development of non-verbal child communication skills. However, most of the advanced AAC, such as Speech Generating Devices (SGD), Picture Exchange Communication System (PECS) based mobile applications (Android & iOS) are designed considering the scenario of developed countries and less accessible in developing countries. Therefore, in this study, we are focusing on representing feasible short term strategies, prospective challenges and as long term strategy, a cloud based framework entitled as "Bolte Chai+", which is an intelligent integrated collaborative learning platform for non-verbal children, parents, caregivers, teachers and SLT. The intelligent analytics within the platform monitors child overall progress by tracking child activity in mobile application and conversely support parents and AAC team to concentrate on individual child ubiquitous abilities. We believe, the proposed framework and strategies will empower non-verbal children and assist researchers, policy makers to acknowledge a definitive solution to implement AAC as communication support in developing countries during COVID-19 pandemic. © 2020, Springer Nature Switzerland AG.

关键词： intelligent systems

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Trustworthiness assessment in multimodal human-robot interaction based on cognitive load

Trustworthiness assessment in multimodal human-robot interac...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Murat Kirtay Erhan Oztop Anna K. Kuhlen Minoru Asada Verena V. Hafner Department of Computer Science Adaptive Systems Group Humboldt-Universität zu Berlin Berlin Germany Department of Psychology Humboldt-Universität zu Berlin Berlin Germany Symbiotic Intelligent Systems Research Center Institute for Open and Transdisciplinary Research Initiatives Osaka University Japan Ozyegin University Istanbul Turkey International Professional University of Technology in Osaka Osaka Japan

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

In this study, we extend our robot trust model into a multimodal setting in which the Nao robot leverages audio-visual data to perform a sequential multimodal pattern recalling task while interacting with a human partner who has different guiding strategies: reliable, unreliable, and random. Here, the humanoid robot is equipped with a multimodal auto-associative memory module to process audio-visual patterns to extract cognitive load (i.e., computational cost) and an internal reward module to perform cost-guided reinforcement learning. After interactive experiments, the robot associates a low cognitive load (i.e., high cumulative reward) yielded during the interaction with high trustworthiness of the guiding strategy of the partner. At the end of the experiment, we provide a free choice to the robot to select a trustworthy instructor. We show that the robot forms trust in a reliable partner. In the second setting of the same experiment, we endow the robot with an additional simple theory of mind module to assess the efficacy of the instructor in helping the robot perform the task. Our results show that the performance of the robot is improved when the robot bases its action decisions on factoring in the instructor assessment.

关键词： Biological system modeling Humanoid robots Reinforcement learning Memory modules Reliability theory Data models Proposals

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Healthier and Independent Living of the Elderly: Interoperability in a Cross-Project Pilot

Healthier and Independent Living of the Elderly: Interoperab...

引用

2022 Interoperability for Enterprise systems and Applications Workshops, I-ESA Workshops 2022

作者： Agostinho, Carlos Pimenta, Andreia Marques, Maria Tsiouris, Kostas M. Kalatzis, Fanis Nikitas, Christos Iliadou, Eleftheria Occhipinti, Mattia Kouris, Ioannis Koutsouris, Dimitrios Basdekis, Ioannis Koloutsou, Konstantina Gallo, Luigi de Pietro, Giueppe Jardim-Goncalves, Ricardo FCT-Campus Caparica2829-516 Portugal NOVA School of Science and Technology Caparica2829-516 Portugal Secretaria Regional de Saúde e Ptroteção Civil Rua da Carreira nº107 Funchal9000-042 Portugal Unit of Medical Technology and Intelligent Information Systems Dept. of Material Science and Engineering University of Ioannina IoanninaGR45110 Greece National Kapodistrian University of Athens First Department of Otolaryngology-Head and Neck Surgery ‘Hippokrateio’ General Hospital Athens Greece Università degli Studi di Milano Computer Science Department Milan Italy Biomedical Engineering Laboratory Institute of Communication and Computer Systems National Technical University of Athens Heroon Polytechneiou 9 15780 Greece SPHYNX Technology Solutions AG Zug Switzerland National Research Council of Italy Via P. Bucci cubo 8/9C Rende87036 Italy

The ageing of the population creates new heterogeneous challenges for age-friendly living. The progressive decline in physical and cognitive skills tends to prevent elderly people from performing basic instrumental activities of daily living and there is a growing interest in technology for aging support. Digital health today can be exercised by anyone owning a smartphone and parameters such as heart rate, step counts, calorie intake, sleep quality, can be collected and used not only to monitor and improve the individual’s health condition but also to prevent illnesses. However, for the benefits of e-health to take place, digital health data, either Electronic Health Records (EHR) or sensor data from the IoMT, must be shared, maintaining privacy and security requirements but unlocking the potential for research an innovation throughout EU. This paper demonstrates the added value of such interoperability requirements, and a form of accomplishing them through a cross-project pilot. © 2022 CEUR-WS. All rights reserved.

关键词： Interoperability

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An ensemble deep learning model for predicting minimum inhibitory concentrations of antimicrobial peptides against pathogenic bacteria

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iscience 2024年第9期27卷

作者： Chung, Chia-Ru Chien, Chung-Yu Tang, Yun Wu, Li-Ching Hsu, Justin Bo-Kai Lu, Jang-Jih Lee, Tzong-Yi Bai, Chen Horng, Jorng-Tzong Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Sciences and Engineering National Central University Taoyuan Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Department of Laboratory Medicine Chang Gung Memorial Hospital at Linkou Taoyuan City Taiwan School of Medicine Chang Gung University Taoyuan City Taiwan Department of Medical Biotechnology and Laboratory Science Chang Gung University Taoyuan City Taiwan Center for Intelligent Drug Systems and Smart Biodevices (IDS2B) National Yang Ming Chiao Tung University Hsinchu City Taiwan Warshel Institute for Computational Biology School of Medicine The Chinese University of Hong Kong (Shenzhen) Shenzhen 518172 Hong Kong

The rise of antibiotic resistance necessitates effective alternative therapies. Antimicrobial peptides (AMPs) are promising due to their broad inhibitory effects. This study focuses on predicting the minimum inhibitory concentration (MIC) of AMPs against whom-priority pathogens: Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, and Pseudomonas aeruginosa ATCC 27853. We developed a comprehensive regression model integrating AMP sequence-based and genomic features. Using eight AI-based architectures, including deep learning with protein language model embeddings, we created an ensemble model combining bi-directional long short-term memory (BiLSTM), convolutional neural network (CNN), and multi-branch model (MBM). The ensemble model showed superior performance with Pearson correlation coefficients of 0.756, 0.781, and 0.802 for the bacterial strains, demonstrating its accuracy in predicting MIC values. This work sets a foundation for future studies to enhance model performance and advance AMP applications in combating antibiotic resistance. © 2024 The Authors

关键词： Biological sciences Chemistry computer science

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