Explore an active learning application in the search for dark matter using ATLAS PanDA and iDDS. Investigate Beyond Standard Model physics parameters related to Hidden Abelian Higgs Model and New Scalar with a focus on cross-section limit calculations. Understand the process for generating Monte Car

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System models play a crucial role in software engineering, aiding in understanding system functionality and communicating with customers. They include context models, behavioural models, data models, object models, and more, each offering unique perspectives on the system. Different types of system

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Explore the process of adding publisher/place of publication as a search parameter in Library Search, with insights on using MARC fields, establishing normalization rules, and steps to enable and translate local fields for effective faceted searching in Primo VE. Learn about the nuances of field rec

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Explore the concept of parameter contribution in machine learning models and discuss the importance of balancing parameters for optimal performance. Introduce an intra-distillation method to train and utilize potentially redundant parameters effectively. A case study on knowledge distillation illust

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This document presents recommendations for parameter and feature sets to enhance the NBA-UWB MMS operations, focusing on lowering testing costs and enabling smoother interoperations. Key aspects covered include interference mitigation techniques, coexistence improvements, enhanced ranging capabiliti

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Travelopro Global Distribution System (GDS) is a computerized network system. It is a large computer network which is integrated with 100 of worldwide Airlines and consolidators for enabling transactions between travel agents and travel sites and also used by airlines, hotels, car rentals, railways

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Parameter Expression Calculator within HEC-HMS offers a convenient tool to estimate loss, transform, and baseflow parameters using GIS data. It includes various options such as Deficit and Constant Loss, Green and Ampt Transform, Mod Clark Transform, Clark Transform, S-Graph, and Linear Reservoir. U

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Classical Mechanics explores the Variational Principle in the calculus of variations, offering a method to determine maximum values of quantities dependent on functions. This principle, rooted in the wave function, aids in finding parameter values such as expectation values independently of the coor

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System modeling in engineering involves developing abstract models to represent a system from various perspectives using graphical notations like UML. These models aid in understanding system functionality, communicating with stakeholders, and documenting requirements for new systems. Existing and p

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This document delves into the IEEE 802.11-21/0036r0 standard, specifically focusing on the BSS parameter update procedure within TGbe D0.2. It details how an AP within an AP MLD transmits Change Sequence fields, Critical Update Flags, and other essential elements in Beacon and Probe Response frames.

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The root locus method in control systems involves tracing the path of roots of the characteristic equation in the s-plane as a system parameter varies. This technique simplifies the analysis of closed-loop stability by plotting the roots for different parameter values. With the root locus method, de

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Sensitivity analysis in electric power systems involves examining the impact of parameter changes on system behavior. This lecture discusses linearized sensitivity analysis, matrix notation, injection shift factors (ISFs), and more, providing insights into system reliability and security. Concepts l

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Development of Learning Techniques in Automation Control Systems at the National Technical University of Athens focuses on system identification, parameter approximation, and achieving control goals using statistical methods and mathematical models. Techniques such as open loop form, closed loop for

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S-Parameter measurements in microwave engineering are typically conducted using a Vector Network Analyzer (VNA) to analyze the behavior of devices under test (DUT) at microwave frequencies. These measurements involve the use of error boxes, calibration techniques, and de-embedding processes to extra

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Subprograms in software development provide a means for abstraction and modularity, with characteristics like single entry points, suspension of calling entities, and return of control upon termination. They encompass procedures and functions, raising design considerations such as parameter passing

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Noise, an unwanted input, limits a system's ability to process weak signals. Sources of noise include random noise in resistors and transistors, mixer noise, undesired cross-coupling noise, and power supply noise. Thermal noise, caused by thermal agitation of charge carriers, is also known as Johnso

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System Sequence Diagrams (SSDs) are vital artifacts that visually illustrate input and output events related to a system. They help define system behavior and interactions, making them essential during the logical design phase of software applications. By depicting events in sequential order, SSDs o

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Improving human-computer interactions with gamification can help understand ecological sustainability better by parameterizing complex models. Allometric Trophic Network models analyze energy flow and biomass dynamics, but face challenges in parameterization. The Convergence Game in World of Balance

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Surface soil moisture retrieval is crucial for various applications such as climatic modeling, hydrological studies, and agronomy. This work focuses on developing a soil moisture retrieval algorithm using the SAOCOM L-Band polarimetric SAR system in Argentina. Limiting factors include spatial variab

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This code assignment focuses on deducing the radius parameter (r0) for Odd-A and Odd-Odd nuclei by utilizing even-even radii data from 1998Ak04 input. Developed by Sukhjeet Singh and Balraj Singh, the code utilizes a specific deduction procedure to calculate radius parameters for nuclei falling with

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In the field of information retrieval, learning to rank involves optimizing ranking functions using various models like VSM, PageRank, and more. Parameter tuning is crucial for optimizing ranking performance, treated as an optimization problem. The ranking process is viewed as a learning problem whe

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Clustering is a fundamental pre-Deep Learning Machine Learning method for grouping similar data points. This paper introduces an innovative parameter-free clustering algorithm that eliminates the need for human-assigned parameters, such as the target number of clusters (K). By leveraging first neigh

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The CEPC Partial Double Ring Layout features advantages like accommodating more bunches at Z/W energy, reducing AC power with crab waist collision, and unique machine constraints based on given parameters. The provided parameter choices and updates aim to optimize beam-beam effects, emittance growth

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BlinkDB is a system that focuses on organizing sampling around query column sets and determining query classes with the best efficiency. However, potential failures lie in unstable QCSes, high rare subgroup counts, and challenging dimensionality. Drawbacks include unclear parameter tuning, limited o

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Explore the foundations of parameter estimation and decision theory in machine learning through topics such as frequentist estimation, properties of estimators, Bayesian parameter estimation, and maximum likelihood estimator. Understand concepts like consistency, bias-variance trade-off, and the Bay

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Statistical inference involves acquiring information and drawing conclusions about populations from samples using estimation and hypothesis testing. Estimation determines population parameter values based on sample statistics, utilizing point and interval estimators. Interval estimates, known as con

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This document discusses abstraction in system-level simulation for High-Efficiency Wireless (HEW) study, focusing on effective Signal-to-Noise Ratio (SNR) mapping, parameter fitting, selection of mapping functions, and simulation conditions/assumptions for IEEE 802.11. The study explores various met

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Learn about sampling and parameter fitting with Hawkes processes in the context of human-centered machine learning. Understand the importance of fitting parameters and sampling raw data event times. Explore the characteristics and fitting methods of Hawkes processes, along with coding assignments an

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This informative content covers the concepts of linear classifiers and Naive Bayes models in text classification. It discusses obtaining parameter values, indexing in Bag-of-Words, different algorithms, feature representations, and parameter learning methods in detail.

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Delve into the process of tuning check and parameter reconstruction through a series of informative images depicting old tuning parameters and data sets. Explore how 18 data and 18 MC as well as 18 MC and 12 MC old tuning parameters play a crucial role in optimizing performance and accuracy. Gain va

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Submission in September 2017 proposes improvements in TWT parameter selection for IEEE 802.11 networks. It allows TWT requesting STAs to signal repeat times, enhancing transmission reliability and reducing overheads. Non-AP STA challenges and current TWT setup signaling are addressed, providing a me

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Explore the extension of linear elastic material model to a Cosserat material, including microrotation degrees of freedom, with a focus on a cylindrical bar under pure torsion. Investigate the impact of Cosserat length scale parameter on the system response, analyzing different zones of behavior. Re

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Designing a scalable query system for evaluating complex game environments involves key elements like defining required features, structuring query elements, and understanding function models for optimal performance. The system must be customizable, support debugging, and allow runtime parameter adj

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Sage ERP X3 allows users to define specific parameters at the user level, overriding values set at the folder or company level. This feature enables customization of settings tailored to individual users, enhancing user experience and efficiency within the system. Explore the various user parameter

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IGEL Universal Management Suite (UMS) is a comprehensive system that empowers IT professionals with easy control over all endpoints. Included features like centralized installation, configuration, and update management make UMS a versatile tool for managing IGEL Zero Clients and third-party endpoint

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Korea-Bulgaria Knowledge Sharing Program focused on sharing strategies to enhance Bulgaria's Extended Producer Responsibility (EPR) system. The program discusses the current status, performance evaluation, issues, and recommendations related to the EPR system in Korea. It covers topics like beverage

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This research focuses on hyper-parameter tuning for graph kernels using Multiple Kernel Learning, emphasizing the importance of kernel methods in learning on structured data like graphs. It explores techniques applicable to various domains and discusses different graph kernels and their sub-structur

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The evolution of metalearning in the machine learning community is traced from the initial workshop in 1998 to recent developments in hyper-parameter optimization. Challenges in classifier selection and the validity of hyper-parameter optimization claims are discussed, urging the exploration of spec

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Confidence limits are a crucial concept in statistical analysis, representing the upper and lower boundaries of confidence intervals. They provide a range of values around a sample statistic within which the true parameter is expected to lie with a certain probability. By calculating these limits, r

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Confidence limits are commonly used to summarize the probability distribution of errors in parameter estimation. Experimenters choose both the confidence level and shape of the confidence region, with customary percentages like 68.3%, 95.4%, and 99%. Ellipses or ellipsoids are often used in higher d

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