{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/management/papers/67","list_of":"/task/management","task":"Management","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":67,"pages_in_order":82,"rows_per_page":100,"rows":[6601,6700],"of":8144,"counts":{"archive_papers_tagged":8144,"with_a_code_link":1214,"where_syntology_ran_a_sample":119,"not_listed_spam_title":0,"listed":8144,"listed_where_code_ran":119,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":103,"every_run_a_failure_of_syntologys_instrument":16,"listed_with_a_run_with_no_instrument_failure":103,"listed_every_run_a_failure_of_syntologys_instrument":16,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/management","prev":"/task/management/papers/66","next":"/task/management/papers/68","papers":[{"url":null,"slug":"therapeutic-management-of-sacro-coccygeal","title":"Therapeutic Management of Sacro-coccygeal compression in a Green Iguana (Iguana iguana)","date":"2020-04-11","arxiv_id":"2004.14296","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-single-cell-rna-expression-map-of","title":"A single-cell RNA expression map of coronavirus receptors and associated factors in developing human embryos","date":"2020-04-10","arxiv_id":"2004.04935","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-approach-for-determining","title":"A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants","date":"2020-04-08","arxiv_id":"2004.04023","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-management-for-blockchain-enabled","title":"Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach","date":"2020-04-08","arxiv_id":"2004.04104","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-german-corpus-for-fine-grained-named-entity-2","title":"A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events","date":"2020-04-07","arxiv_id":"2004.03283","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generalized-multi-task-learning-approach-to","title":"A Generalized Multi-Task Learning Approach to Stereo DSM Filtering in Urban Areas","date":"2020-04-06","arxiv_id":"2004.02493","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-analysis-of-multiple-deep-cnn","title":"Comparative Analysis of Multiple Deep CNN Models for Waste Classification","date":"2020-04-05","arxiv_id":"2004.02168","repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-processing-for-word-sense","title":"Natural language processing for word sense disambiguation and information extraction","date":"2020-04-05","arxiv_id":"2004.02256","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-ensemble-multi-agent-reinforcement","title":"A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air Traffic Control","date":"2020-04-03","arxiv_id":"2004.01387","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-rice-blast-disease-machine","title":"Predicting rice blast disease: machine learning versus process based models","date":"2020-04-03","arxiv_id":"2004.01602","repositories_listed":0,"syntology":null},{"url":null,"slug":"asymptotically-optimal-management-of","title":"Asymptotically Optimal Management of Heterogeneous Collectivised Investment Funds","date":"2020-04-02","arxiv_id":"2004.01506","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-probabilistic-modelling-of-price","title":"Deep Probabilistic Modelling of Price Movements for High-Frequency Trading","date":"2020-03-31","arxiv_id":"2004.01498","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bayesian-approach-to-recover-the","title":"A Bayesian approach to recover the theoretical temperature-dependent hatch date distribution from biased samples: the case of the common dolphinfish (Coryphaena hippurus)","date":"2020-03-30","arxiv_id":"2004.01000","repositories_listed":0,"syntology":null},{"url":null,"slug":"bounding-privacy-leakage-in-smart-buildings","title":"Bounding Privacy Leakage in Smart Buildings","date":"2020-03-30","arxiv_id":"2003.13187","repositories_listed":0,"syntology":null},{"url":null,"slug":"concept-aware-geographic-information","title":"Concept-aware Geographic Information Retrieval","date":"2020-03-30","arxiv_id":"2003.13481","repositories_listed":0,"syntology":null},{"url":null,"slug":"making-metadata-fit-for-next-generation","title":"Making Metadata Fit for Next Generation Language Technology Platforms: The Metadata Schema of the European Language Grid","date":"2020-03-30","arxiv_id":"2003.13236","repositories_listed":0,"syntology":null},{"url":null,"slug":"comfort-as-a-service-designing-a-user","title":"Comfort-as-a-Service: Designing a User-Oriented Thermal Comfort Artifact for Office Buildings","date":"2020-03-27","arxiv_id":"2004.03323","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multivariate-water-quality-parameter","title":"A multivariate water quality parameter prediction model using recurrent neural network","date":"2020-03-25","arxiv_id":"2003.11492","repositories_listed":0,"syntology":null},{"url":null,"slug":"cryptocurrency-trading-a-comprehensive-survey","title":"Cryptocurrency Trading: A Comprehensive Survey","date":"2020-03-25","arxiv_id":"2003.11352","repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-to-schedule-leasch-a-deep-reinforcement","title":"Learn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer","date":"2020-03-24","arxiv_id":"2003.11003","repositories_listed":0,"syntology":null},{"url":null,"slug":"tracer-a-framework-for-facilitating-accurate","title":"TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications","date":"2020-03-24","arxiv_id":"2003.12012","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridge-the-domain-gap-between-ultra-wide","title":"Bridge the Domain Gap Between Ultra-wide-field and Traditional Fundus Images via Adversarial Domain Adaptation","date":"2020-03-23","arxiv_id":"2003.10042","repositories_listed":0,"syntology":null},{"url":null,"slug":"equity-factors-to-short-or-not-to-short-that","title":"Equity Factors: To Short Or Not To Short, That Is The Question","date":"2020-03-23","arxiv_id":"2003.10419","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-energy-aware-online-learning-framework-for","title":"An Energy-Aware Online Learning Framework for Resource Management in Heterogeneous Platforms","date":"2020-03-20","arxiv_id":"2003.09526","repositories_listed":0,"syntology":null},{"url":null,"slug":"combined-cooling-heating-and-power-system-in","title":"Combined Cooling, Heating, and Power System in Blockchain-Enabled Energy Management","date":"2020-03-20","arxiv_id":"2003.13416","repositories_listed":0,"syntology":null},{"url":null,"slug":"engine-and-aftertreatment-co-optimization-of","title":"Engine and Aftertreatment Co-Optimization of Connected HEVs via Multi-Range Vehicle Speed Planning and Prediction","date":"2020-03-20","arxiv_id":"2003.09438","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-bias-correction-for-ultra","title":"Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots","date":"2020-03-20","arxiv_id":"2003.09371","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrated-power-and-thermal-management-of","title":"Integrated Power and Thermal Management of Connected HEVs via Multi-Horizon MPC","date":"2020-03-19","arxiv_id":"2003.08855","repositories_listed":0,"syntology":null},{"url":null,"slug":"systemic-risk-statistics-with-scenario","title":"Complex risk statistics with scenario analysis","date":"2020-03-19","arxiv_id":"2003.09255","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-of-orofacial-kinematics-in","title":"Estimation of Orofacial Kinematics in Parkinson's Disease: Comparison of 2D and 3D Markerless Systems for Motion Tracking","date":"2020-03-18","arxiv_id":"2003.08048","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-intelligence-approach-to-momentum","title":"Artificial intelligence approach to momentum risk-taking","date":"2020-03-17","arxiv_id":"1911.08448","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-control-of-multi-zone-hvac","title":"Distributed Control of Multi-zone HVAC Systems Considering Indoor Air Quality","date":"2020-03-17","arxiv_id":"2003.08208","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-quality-transfer-enhances-contrast-and","title":"Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients","date":"2020-03-16","arxiv_id":"2003.07216","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-and-machine-learning-to-forecast","title":"Time series and machine learning to forecast the water quality from satellite data","date":"2020-03-16","arxiv_id":"2003.11923","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-collaborative-approach-to-decision","title":"Towards a Collaborative Approach to Decision Making Based on Ontology and Multi-Agent System Application to crisis management","date":"2020-03-16","arxiv_id":"2003.07096","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-for-demand","title":"Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing","date":"2020-03-16","arxiv_id":"2003.07017","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-stackelberg-game-approach-to-resource","title":"A Stackelberg Game Approach to Resource Allocation for Intelligent Reflecting Surface Aided Communications","date":"2020-03-14","arxiv_id":"2003.06640","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-deep-q-network-in-portfolio","title":"Application of Deep Q-Network in Portfolio Management","date":"2020-03-13","arxiv_id":"2003.06365","repositories_listed":0,"syntology":null},{"url":null,"slug":"managing-aquatic-invasions-optimal-locations","title":"Managing aquatic invasions: optimal locations and operating times for watercraft inspection stations","date":"2020-03-13","arxiv_id":"2003.06092","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-guest-detection-in-a-smart-home-using","title":"Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning","date":"2020-03-13","arxiv_id":"2003.06347","repositories_listed":0,"syntology":null},{"url":null,"slug":"smart-home-energy-management-system-for-power","title":"Smart Home Energy Management System for Power System Resiliency","date":"2020-03-12","arxiv_id":"2003.05570","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-intelligent-optical","title":"Machine Learning for Intelligent Optical Networks: A Comprehensive Survey","date":"2020-03-11","arxiv_id":"2003.05290","repositories_listed":0,"syntology":null},{"url":null,"slug":"wireless-communication-for-safe-uavs-from","title":"Wireless communication for safe UAVs: From Long-Range Deconfliction to short-range collision avoidance","date":"2020-03-11","arxiv_id":"1910.13744","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-warehouse-and-decision-support-on","title":"Data Warehouse and Decision Support on Integrated Crop Big Data","date":"2020-03-10","arxiv_id":"2003.04470","repositories_listed":0,"syntology":null},{"url":null,"slug":"mobility-management-for-cellular-connected","title":"Mobility Management for Cellular-Connected UAVs: A Learning-Based Approach","date":"2020-03-10","arxiv_id":"2002.01546","repositories_listed":0,"syntology":null},{"url":null,"slug":"phase-based-variant-maximum-likelihood","title":"Phase-based Variant Maximum Likelihood Positioning for Passive UHF-RFID Tags","date":"2020-03-10","arxiv_id":"2003.05029","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-cost-management-in-smart-meters-using","title":"Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning","date":"2020-03-10","arxiv_id":"2003.04946","repositories_listed":0,"syntology":null},{"url":null,"slug":"copula-based-local-dependence-between-energy","title":"Copula-based local dependence between energy, agriculture and metal commodity markets","date":"2020-03-09","arxiv_id":"2003.04007","repositories_listed":0,"syntology":null},{"url":null,"slug":"link-prediction-using-graph-neural-networks","title":"Link Prediction using Graph Neural Networks for Master Data Management","date":"2020-03-07","arxiv_id":"2003.04732","repositories_listed":0,"syntology":null},{"url":null,"slug":"change-point-models-for-real-time-cyber","title":"Change Point Models for Real-time Cyber Attack Detection in Connected Vehicle Environment","date":"2020-03-05","arxiv_id":"2003.04185","repositories_listed":0,"syntology":null},{"url":null,"slug":"demographic-bias-in-biometrics-a-survey-on-an","title":"Demographic Bias in Biometrics: A Survey on an Emerging Challenge","date":"2020-03-05","arxiv_id":"2003.02488","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-system-id-optimal-management-of","title":"Bayesian System ID: Optimal management of parameter, model, and measurement uncertainty","date":"2020-03-04","arxiv_id":"2003.02359","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-aware-augmented-reality","title":"Spatiotemporal-Aware Augmented Reality: Redefining HCI in Image-Guided Therapy","date":"2020-03-04","arxiv_id":"2003.02260","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-machine-learning-applications-in-1","title":"A review of machine learning applications in wildfire science and management","date":"2020-03-02","arxiv_id":"2003.00646","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-pre-processing-and-evaluating-the","title":"Data Pre-Processing and Evaluating the Performance of Several Data Mining Methods for Predicting Irrigation Water Requirement","date":"2020-03-01","arxiv_id":"2003.00411","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-self-adapting-particle-swarm","title":"Generalized Self-Adapting Particle Swarm Optimization algorithm with archive of samples","date":"2020-02-28","arxiv_id":"2002.12485","repositories_listed":0,"syntology":null},{"url":null,"slug":"extending-the-risc-v-isa-for-efficient-rnn","title":"Extending the RISC-V ISA for Efficient RNN-based 5G Radio Resource Management","date":"2020-02-27","arxiv_id":"2002.12877","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-aware-network-topology-management","title":"Resource-Aware Network Topology Management Framework","date":"2020-02-26","arxiv_id":"2003.00860","repositories_listed":0,"syntology":null},{"url":null,"slug":"g-learner-and-girl-goal-based-wealth","title":"G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning","date":"2020-02-25","arxiv_id":"2002.10990","repositories_listed":0,"syntology":null},{"url":null,"slug":"wireless-20-towards-an-intelligent-radio","title":"Wireless 2.0: Towards an Intelligent Radio Environment Empowered by Reconfigurable Meta-Surfaces and Artificial Intelligence","date":"2020-02-23","arxiv_id":"2002.11040","repositories_listed":0,"syntology":null},{"url":null,"slug":"sampling-for-deep-learning-model-diagnosis","title":"Sampling for Deep Learning Model Diagnosis (Technical Report)","date":"2020-02-22","arxiv_id":"2002.09754","repositories_listed":0,"syntology":null},{"url":null,"slug":"robusttad-robust-time-series-anomaly","title":"RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks","date":"2020-02-21","arxiv_id":"2002.09545","repositories_listed":0,"syntology":null},{"url":null,"slug":"cleaner-production-in-optimized-multivariate","title":"Cleaner Production in Optimized Multivariate Networks: Operations Management through a Roll of Dice","date":"2020-02-20","arxiv_id":"2003.00884","repositories_listed":0,"syntology":null},{"url":null,"slug":"riskoracle-a-minute-level-citywide-traffic","title":"RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework","date":"2020-02-19","arxiv_id":"2003.00819","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-scalable-method-for-scheduling-distributed","title":"A Scalable Method for Scheduling Distributed Energy Resources using Parallelized Population-based Metaheuristics","date":"2020-02-18","arxiv_id":"2002.07505","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-efficient-runtime-resource-management","title":"Energy-efficient Runtime Resource Management for Adaptable Multi-application Mapping","date":"2020-02-18","arxiv_id":"2001.08094","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-term-traffic-flow-prediction-using","title":"Short-Term Traffic Flow Prediction Using Variational LSTM Networks","date":"2020-02-18","arxiv_id":"2002.07922","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-sectional-stock-price-prediction-using","title":"Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management","date":"2020-02-17","arxiv_id":"2002.06975","repositories_listed":0,"syntology":null},{"url":null,"slug":"monotonic-cardinality-estimation-of","title":"Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach","date":"2020-02-15","arxiv_id":"2002.06442","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-management-in-wireless-networks-via","title":"Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning","date":"2020-02-14","arxiv_id":"2002.06215","repositories_listed":0,"syntology":null},{"url":null,"slug":"traffic-modelling-and-prediction-via-symbolic","title":"Traffic Modelling and Prediction via Symbolic Regression on Road Sensor Data","date":"2020-02-14","arxiv_id":"2002.06095","repositories_listed":0,"syntology":null},{"url":null,"slug":"trends-of-digitalization-and-adoption-of-big","title":"Trends of digitalization and adoption of big data & analytics among UK SMEs: Analysis and lessons drawn from a case study of 53 SMEs","date":"2020-02-14","arxiv_id":"2002.11623","repositories_listed":0,"syntology":null},{"url":null,"slug":"workload-prediction-of-business-processes-an","title":"Workload Prediction of Business Processes -- An Approach Based on Process Mining and Recurrent Neural Networks","date":"2020-02-14","arxiv_id":"2002.11675","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-to-unseen-environments-through","title":"Adapting to Unseen Environments through Explicit Representation of Context","date":"2020-02-13","arxiv_id":"2002.05640","repositories_listed":0,"syntology":null},{"url":null,"slug":"keyphrase-extraction-with-span-based-feature","title":"Keyphrase Extraction with Span-based Feature Representations","date":"2020-02-13","arxiv_id":"2002.05407","repositories_listed":0,"syntology":null},{"url":null,"slug":"guiding-the-guiders-foundations-of-a-market","title":"Guiding the guiders: Foundations of a market-driven theory of disclosure","date":"2020-02-12","arxiv_id":"2002.04886","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-driven-heterogeneous-mec-system-with-uav","title":"AI Driven Heterogeneous MEC System with UAV Assistance for Dynamic Environment -- Challenges and Solutions","date":"2020-02-11","arxiv_id":"2002.05020","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-footprint-generation-by","title":"Building Footprint Generation by IntegratingConvolution Neural Network with Feature PairwiseConditional Random Field (FPCRF)","date":"2020-02-11","arxiv_id":"2002.04600","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-approach-to-automate-high","title":"A Deep Learning Approach to Automate High-Resolution Blood Vessel Reconstruction on Computerized Tomography Images With or Without the Use of Contrast Agent","date":"2020-02-09","arxiv_id":"2002.03463","repositories_listed":0,"syntology":null},{"url":null,"slug":"splitting-convolutional-neural-network","title":"Splitting Convolutional Neural Network Structures for Efficient Inference","date":"2020-02-09","arxiv_id":"2002.03302","repositories_listed":0,"syntology":null},{"url":null,"slug":"trust-in-data-science-collaboration","title":"Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects","date":"2020-02-09","arxiv_id":"2002.03389","repositories_listed":0,"syntology":null},{"url":null,"slug":"comprehensive-and-efficient-data-labeling-via","title":"Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling","date":"2020-02-08","arxiv_id":"2002.05520","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrated-optimization-of-railway-freight","title":"Integrated optimization of railway freight operation planning and pricing based on carbon emission reduction policies","date":"2020-02-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-energy-dispatch-in-isolated","title":"Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgrids","date":"2020-02-07","arxiv_id":"2002.02581","repositories_listed":0,"syntology":null},{"url":null,"slug":"equivalence-relations-and-lp-distances","title":"Equivalence relations and $L^p$ distances between time series with application to the Black Summer Australian bushfires","date":"2020-02-07","arxiv_id":"2002.02592","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-temporal-resolution-rainfall-runoff","title":"High Temporal Resolution Rainfall Runoff Modelling Using Long-Short-Term-Memory (LSTM) Networks","date":"2020-02-07","arxiv_id":"2002.02568","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-term-blood-glucose-prediction-based-on","title":"Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data","date":"2020-02-06","arxiv_id":"2002.02805","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-image-based-identification-and","title":"Automatic image-based identification and biomass estimation of invertebrates","date":"2020-02-05","arxiv_id":"2002.03807","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-crowd-behaviors-in-a-social","title":"Understanding Crowd Behaviors in a Social Event by Passive WiFi Sensing and Data Mining","date":"2020-02-05","arxiv_id":"2002.04401","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoupling-learning-rates-using-empirical","title":"Bayesian Meta-Prior Learning Using Empirical Bayes","date":"2020-02-04","arxiv_id":"2002.01129","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-allocation-based-soft-load-shedding","title":"Fair Allocation Based Soft Load Shedding","date":"2020-02-02","arxiv_id":"2002.00451","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-weighted-causal-graphs","title":"Uncertainty Weighted Causal Graphs","date":"2020-02-02","arxiv_id":"2002.00429","repositories_listed":0,"syntology":null},{"url":null,"slug":"dialogue-based-simulation-for-cultural","title":"Dialogue-Based Simulation For Cultural Awareness Training","date":"2020-02-01","arxiv_id":"2002.00223","repositories_listed":0,"syntology":null},{"url":null,"slug":"constrained-deep-reinforcement-learning-for","title":"Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV based Random Access IoT Networks with NOMA","date":"2020-01-31","arxiv_id":"2002.00073","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-segmentation-of-cracks-on","title":"Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm","date":"2020-01-30","arxiv_id":"2001.11248","repositories_listed":0,"syntology":null},{"url":null,"slug":"kalibre-knowledge-based-neural-surrogate","title":"Kalibre: Knowledge-based Neural Surrogate Model Calibration for Data Center Digital Twins","date":"2020-01-29","arxiv_id":"2001.10681","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-multi-perspective-conformance","title":"Towards Multi-perspective conformance checking with fuzzy sets","date":"2020-01-29","arxiv_id":"2001.10730","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-well-log-prediction-from-drilling","title":"Real-Time Well Log Prediction From Drilling Data Using Deep Learning","date":"2020-01-28","arxiv_id":"2001.10156","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-of-high-frequency-nutrient","title":"Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods","date":"2020-01-27","arxiv_id":"2001.09695","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-based-autoscaling-of","title":"Reinforcement Learning-based Application Autoscaling in the Cloud: A Survey","date":"2020-01-27","arxiv_id":"2001.09957","repositories_listed":0,"syntology":null}],"record_sha256":"6ee9e27f4dcaf9c6fccea515153a00f4a78ed2a5a41072a484212f2dafe2f15d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}