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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Workshop on Indian Ocean Variability and Teleconnections discussed the assessment of the link between Indian Ocean Dipole (IOD) and rainfall variability in Central Africa. The study utilized precipitation data, sea surface temperatures, and dynamic variables to analyze the impact of IOD events on Ce

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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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fMRI data analysis involves coregistration and spatial normalization to align functional and structural images, reduce variability, and prepare data for statistical analysis. Coregistration aligns images from different modalities within subjects, while spatial normalization achieves precise anatomic

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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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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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Measures of dispersion in statistics help in understanding the variability of data points. They are essential for assessing the reliability of central tendency measures, comparing data sets, identifying variability causes, and guiding further statistical analysis. The properties of a good measure of

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Variability is key in statistical studies, shaping the essence of statistical analysis. Students often struggle to grasp the concept of variability, despite being taught statistical methods. The term "variation" takes on different meanings in various statistical contexts, presenting challenges in co

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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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This lesson covers the concepts of variance and standard deviation as measures of variability in a data set. It explains how deviations from the mean are used to calculate variance, and how standard deviation, as the square root of variance, measures the average distance from the mean. Degree of fre

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Exploring the concept of variability in statistical analysis of one-variable data, focusing on key measures such as range, interquartile range, and standard deviation. Learn how to interpret and calculate these metrics to understand the spread of data points and identify outliers. Utilize quartiles

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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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Explore topics such as temporal and spatial variability in observation error, ensemble spread analysis, baseline observations at DWD, estimation of observation errors, and radar reflectivity analysis. Gain insights into data processing and interpretation in meteorological studies.

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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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Modelling and multi-objective optimization of quality attributes in variability-rich software is crucial for customizing software functionality to meet stakeholders' diverse needs. This involves addressing conflicting quality requirements such as cost, reliability, performance, and binary footprint

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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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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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The study evaluates soil moisture estimates from satellite-based products over two network regions. Soil moisture is crucial for the hydrological cycle, necessitating continuous monitoring due to its spatial and temporal variability. Remote sensing is utilized to gather information without physical

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Addressing challenges in crop production involves managing soil nutrients, moisture content, and variability. Precision agriculture techniques offer solutions such as site-specific nutrient management and mathematical approaches for matching fertilizer recommendations. This chapter discusses sources

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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 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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Research conducted in Hanoi, Vietnam, reveals an increase in lower tropospheric ozone over the eastern edge of the Indochina Peninsula. The study focuses on understanding the three-dimensional distribution and temporal variation of ozone to comprehend air pollution and atmospheric variability. Findi

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Random sampling is crucial in statistical analysis to minimize sampling error. Sampling variability occurs due to chance when a random sample is surveyed instead of the entire population. Different units selected can lead to slightly varied estimates. It's important to understand and address samplin

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Central tendency and variability are fundamental features of statistical distributions. Central tendency, encompassing mean, median, and mode, represents the middle of a distribution, while variability describes the spread of data points. Knowing the effect of distribution shape on these measures he

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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 fascinating world of population genetics, covering key concepts like the history of population genetics, mutation types, measurement of variability, and mutation rates. Delve into the diverse mutations such as substitutions, insertions, deletions, and duplications, along with how these v

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This study explores the genotypic variability in colour, pungency, and antioxidant activity among different chilli genotypes, highlighting the importance of chillies as a major spice crop in India and various other countries. The research aims to analyze the extent of variability in key parameters s

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Fabrication variability in semiconductor manufacturing refers to variations in physical characteristics of transistors, impacting their electrical parameters. This variability, whether systematic or random, can lead to performance deviations in digital circuits, affecting power consumption, timing,

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Understanding the variability of spike trains and sources of variability in neural networks, dissecting if variability is equivalent to noise. Delving into the Poisson model, stochastic spike arrival, and firing, and biological modeling of neural networks. Examining variability in different brain re

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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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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 study explores the impact of monsoon variability on crop irrigation in India using an empirical model. It examines changes in crop irrigated areas in response to monsoon variations, focusing on rice, wheat, and sorghum. The research analyzes crop water needs, sensitivity to drought, and irrigat

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Explore the research on reactive clocks with variability-tracking jitter presented at ICCD 2015. The study delves into the cost of variability margins, timing analysis, clock variability comparison between PLL and reactive clocks, and adaptive frequency utilization. Understand the benefits and impli

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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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Standard deviation measures the variability or spread of measurements in a data set, while standard error of the means quantifies the precision of the mean of a set of means from replicated experiments. Variability is indicated by the range of data values, with low standard deviation corresponding t

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Explore approaches to enhance long-term energy models for integrating variable renewables in long-term planning. Key aspects include improving generation networks, ensuring sufficient capacity and reliability, flexibility, robustness to contingencies, and security considerations. Temporal matching o

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