Explore a novel approach for detecting credit card fraud using a semi-supervised attribute-driven graph representation. The technique leverages temporal aggregation and attention layers to automatically unify heterogeneous categorical attributes and detect fraudulent transactions without label leaka

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This research focuses on a system, known as ATPP, based on deep marked temporal point processes, designed for predicting mobile app usage patterns. By leveraging deep reinforcement learning frameworks and context-aware modules, the system aims to predict the next app a user will open, along with its

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NASA's Earth Applied Sciences Program updates include discussions on NRT data products and user needs from health and air quality programs. Updates on NISAR, TEMPO, SBG, AOS, and LANCE are highlighted, focusing on real-time data products for monitoring various atmospheric parameters. Future near rea

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Explore the realm of temporal data in TSQL queries, delving into the concepts of valid time and transaction time, different types of relations like snapshot and bi-temporal, and the significance of time dimensions in database management. Learn how temporal databases support time-related queries for

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The integration of NLP into data pipelines represents a paradigm shift in data engineering, offering companies a powerful tool to reinvent their data workflows and unlock the full potential of their data. By automating data processing tasks, handling diverse data sources, and fostering a data-driven

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This paper introduces the Localised Adaptive Spatial-Temporal Graph Neural Network model, focusing on the importance of spatial-temporal data modeling in graph structures. The challenges of balancing spatial and temporal dependencies for accurate inference are addressed, along with the use of distri

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In today's data-driven world, organizations rely on robust data engineering pipelines to collect, process, and analyze vast amounts of data efficiently. At the heart of these pipelines lies data wrangling, a critical process that involves cleaning, transforming, and preparing raw data for analysis.

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In current data world, businesses thrive on their ability to harness and interpret vast amounts of data. This data, however, often comes in raw, unstructured forms, riddled with inconsistencies and errors. To transform this chaotic data into meaningful insights, organizations need robust data wrangl

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The spatial and temporal distribution of temperatures plays a crucial role in determining weather patterns, climates, vegetation zones, and wildlife habitats. Factors such as latitude, altitude, distance from the coast, and prevailing winds influence the distribution of temperature both horizontally

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Visual systems utilize saccades to focus on objects, impacting temporal perception. This study explores the effects of saccades and stimulus location on perceived time, presenting findings from an experiment on temporal perception mapping with fixed visual duration. The method of psychometric functi

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Data Governance and Data Analytics play crucial roles in transforming data into knowledge and insights for generating positive impacts on various operational systems. They help bring together disparate datasets to glean valuable insights and wisdom to drive informed decision-making. Managing data ma

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Temporal entities and the modeling of simultaneity in CIDOC CRM are under discussion at the upcoming meeting. The current approach considers the cardinality of certain relations, aiming to streamline the representation of time-spans and spacetime volumes. The evolving perspectives on the spatial com

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This content delves into the intricacies of temporal and spatial information models, covering concepts such as existence, presence, and spatiotemporal relationships. It explores how entities are identified, events are witnessed, and durations are defined within these models. The interplay between ti

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This briefing presents information on storm data and the Storm Events Database provided by NCDC User Engagement Services Branch. It includes details on data contacts, data overview, data flow, and access to the Storm Events Database. The dataset covers a chronological listing of various weather phen

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The Visible Infrared Imaging Radiometer Suite (VIIRS) program, a joint effort between NASA and NOAA, focuses on weather prediction and boat detection using low light imaging data collected at night. The VIIRS system provides global coverage with sensitive instruments and efficient data flow processe

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Explore the impact and role of data utilization in organizations through the investigation of data collection methods, data quality, decision-making processes, reliability of collection methods, factors affecting data quality, and privacy considerations. Two scenarios are presented: data collection

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The GOES-R ABI Aerosol Detection Product (ADP) Validation was conducted by Shobha Kondragunta and Pubu Ciren at the NOAA/NESDIS/STAR workshop in January 2014. The validation process involved testing and validating the ADP product using proxy data at various resolutions for detecting smoke, dust, and

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Explore advanced concepts in temporal point processes through the lens of human-centered machine learning. Topics include marked temporal point processes, independent identically distributed marks, dependent marks, and mutually exciting marks. Learn about stochastic dynamical systems such as the Sus

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Explore the comprehensive approach to obtaining data for spatial and temporal characteristics of plants, animals, weather, geology, and more. Learn about the data needed, sources available, considerations in data collection methods, and ensuring accuracy and precision. Jim Graham from Humboldt State

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Dr. Sanjib Pramanik, an Assistant Professor at Durga College, delves into the fascinating realm of remote sensing for environmental studies. Remote sensing allows data collection from a distance, utilizing methods like air photos and satellite imagery. The environment comprises components such as na

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Measurement plays a crucial role in data centers for fault diagnosis, traffic engineering, and attack detection. This study focuses on the concept of temporal coordination of measurement to overcome issues like reporting overhead and resource wastage. Various examples illustrate the importance of co

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The MIP data on Marble provides information on where to access and process data related to Climate Model Intercomparison Projects (CMIP), including CMIP3 and CMIP5. It explains the data directories for different experiments, temporal resolutions available, and the models involved. It guides on how t

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Delve into the intriguing concepts surrounding temporal parts, paradoxes of time travel, the relationship between time and change, the impossibility of changing the past, and the arguments presented by David Lewis on the logical constraints of time travel. Discover the complexities of time perceptio

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In the context of training recurrent networks, Epochwise Back Propagation Through Time involves dividing the data set into independent epochs, each representing a specific temporal pattern of interest. The start time of each epoch, denoted by 'no', is crucial for capturing the sequential dependencie

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This content delves into the application of deep learning models, such as Sequential Modeler, Feature Extraction, and Discriminator, for processing EEG data from the TUH EEG Corpus. The architecture involves various layers like Convolution, Max Pooling, ReLU activation, and Dropout. It explores temp

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This content delves into the broad-scale and cross-scale ecosystem processes on land and in the ocean, exploring their temporal variations influenced by underlying structures, biodiversity, and climate. It emphasizes the importance of considering structure, function, and climate response simultaneou

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The Law & Race Speaker Series on applying past laws to ongoing wrongs explores the complexities of seeking reparations for historic enslavement. The discussion delves into the inter-temporal considerations, touching on reparation claims by CARICOM, the impact of transatlantic chattel slavery on glob

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Explore the utilization of radiance data in global data assimilation at NOAA/NCEP, focusing on the integration of various satellite instruments like AMSU-A, ATMS, MHS, and more. Recent and upcoming enhancements to the global data assimilation system are discussed, with an emphasis on changes to the

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This study examines the correlation between free magnetic energy and flare productivity in active regions on the Sun. By analyzing data from solar events and magnetic field observations, researchers investigate the temporal variation of free magnetic energy and its impact on flare occurrence. Variou

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Conducted during the Fourth Workshop on Water Resources in Developing Countries, the study focused on simulating discharge in the AbourEgrag watershed in Morocco using the ChyM model. The project objectives included a one-year discharge simulation and evaluation of the model in a flooding case study

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Explore the concepts of temporal data models and time dimensions in databases, covering topics such as data structures, query languages, different timestamp types, valid time, and transaction time. Learn about the importance of supporting various time aspects in database systems and the complexities

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Explore the structure and evolution of the Low-Level Jet (LLJ) phenomenon through combined observations and numerical models. The focus is on the LLJ across the PECAN domain, spatial and temporal variability, heterogeneity with height, continuity across cases, and evening transition effects. Efforts

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Utilizing a range of radiance data sources, NOAA/NCEP has implemented significant upgrades to its Global Data Assimilation System. Recent and upcoming changes focus on improving model levels, introducing new observation errors, and expanding all-sky assimilation capabilities. The future direction in

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The breakout report from the TEMPO Applications Workshop in Huntsville, AL highlights the potential of TEMPO data in improving air quality forecasting. It discusses the measurement of ozone precursors, ozone, and PM2.5 on an hourly scale, emphasizing the value of high spatial and temporal resolution

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The Abstract Semi-Supervised Temporal Action Localization (SS-TAL) framework aims to enhance the generalization capability of action detectors using large-scale unlabeled videos. Despite recent progress, a significant challenge persists due to noisy pseudo-labels hindering efficient learning from ab

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Explore the definitions, relationships, and properties of spacetime volume, temporal information, and their components like time spans and entities. Learn about how events are witnessed and defined within specific spatiotemporal contexts.

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This research focuses on transportation mode recognition and user identification by analyzing GPS and accelerometer data. The study involves data collection from varying conditions with over 500 trips and 150 hours of data, processed using spatio-temporal techniques. Features such as mean, deviation

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This research paper discusses a novel spatio-temporal indexing scheme optimized for managing large-scale GPS data. The study introduces a stochastic process model to simulate user behavior in uploading GPS tracks, leading to a more efficient indexing scheme with smaller size, minimal update efforts,

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Utilizing GPS data to infer human activities presents challenges in bridging the semantic gap between raw data and meaningful activities. This study proposes an algorithm that automatically annotates raw trajectories with user activities, focusing on stops, points of interest, and a gravity model. T

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Delve into the world of Sequential Pattern Mining and Association Rule Mining in educational data analysis. Learn about the two categories of SPM approaches, ARM examples like diaper-beer purchases, and SPM examples such as customer behavior patterns. Discover how these methods extract valuable insi

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