Understanding the Importance of Testing and Optimization
In today's highly competitive business landscape, testing and optimization are crucial for companies that want to maximize growth and profitability. Here's an in-depth look at why testing and optimization should be core parts of your business strategy.
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Enhancing Query Optimization in Production: A Microsoft Journey
Explore Microsoft's innovative approach to query optimization in production environments, addressing challenges with general-purpose optimization and introducing specialized cloud-based optimizers. Learn about the implementation details, experiments conducted, and the solution proposed. Discover how
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AnglE: An Optimization Technique for LLMs by Bishwadeep Sikder
The AnglE model introduces angle optimization to address common challenges like vanishing gradients and underutilization of supervised negatives in Large Language Models (LLMs). By enhancing the gradient and optimization processes, this novel approach improves text embedding learning effectiveness.
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Enhancing Online Game Network Traffic Optimization for Improved Performance
Explore the optimization of online game traffic for enhanced user experience by addressing current issues like lags and disconnections in Speed Dreams 2. Learn about modifying the network architecture, implementing interest management, data compression, and evaluation metrics for a stable gaming env
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Introduction to Optimization in Process Engineering
Optimization in process engineering involves obtaining the best possible solution for a given process by minimizing or maximizing a specific performance criterion while considering various constraints. This process is crucial for achieving improved yields, reducing pollutants, energy consumption, an
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Using Open-Source Optimization Tool for Last-Mile Distribution in Zambia
Explore the utilization of an open-source Dispatch Optimization Tool (DOT) for sustainable, flexible, and cost-effective last-mile distribution in Zambia. The tool aims to reduce costs, optimize delivery routes dynamically, and enhance efficiency in supply chain management. Learn about the benefits,
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Understanding Swarm Intelligence: Concepts and Applications
Swarm Intelligence (SI) is an artificial intelligence technique inspired by collective behavior in nature, where decentralized agents interact to achieve goals. Swarms are loosely structured groups of interacting agents that exhibit collective behavior. Examples include ant colonies, flocking birds,
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DNN Inference Optimization Challenge Overview
The DNN Inference Optimization Challenge, organized by Liya Yuan from ZTE, focuses on optimizing deep neural network (DNN) models for efficient inference on-device, at the edge, and in the cloud. The challenge addresses the need for high accuracy while minimizing data center consumption and inferenc
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Understanding Multi-AP Operation in IEEE 802.11-20-0617/r3
Explore the basic definitions and key features of Multi-AP operation in the IEEE 802.11 standard. Learn about Multi-AP Candidate Set (M-AP-CS) and Multi-AP Operation Set (M-AP-OS) along with their participants and formation. Delve into the concepts of Coordinator AP, Coordinated AP(s), and reliable
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Machine Learning Meets Wi-Fi 7: Multi-Link Traffic Allocation-Based RL Use Case
The paper discusses the application of a Reinforcement Learning algorithm, Multi-Headed Recurrent Soft-Actor Critic, for optimizing traffic allocation in IEEE 802.11be Multi-Link Operation networks. This work aims to enhance throughput and reduce latency in MLO-capable devices by distributing incomi
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IEEE 802.11-2020 Multi-Link Reference Model Discussion
This contribution discusses the reference model to support multi-link operation in IEEE 802.11be and proposes architecture reference models to support multi-link devices. It covers aspects such as baseline architecture reference models, logical entities in different layers, Multi-Link Device (MLD) f
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IEEE 802.11-23/1980r1 Coordinated AP-assisted Medium Synchronization Recovery
This document from December 2023 discusses medium synchronization recovery leveraging multi-AP coordination for multi-link devices. It covers features such as Multi-link device (MLD), Multi-link operation (MLO), and Ultra High Reliability (UHR) capability defined in P802.11bn for improvements in rat
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Understanding Multi-Band Multi-Channel Concept in IEEE 802.11be
Exploring the benefits of Multi-Band Multi-Channel (MBMC) operation in IEEE 802.11be, this study delves into the efficient use of spectrum, increased data rates, and network load balancing. It also discusses the envisioned usage models and compares Single Band Operation with Multi-Band Operation, hi
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Multiple Objective Linear Programming: Decision Analysis and Optimization
Explore the complexities of multiple objective linear programming, decision-making with multiple objectives, goal programming, and evolutionary multi-objective optimization. Discover the trade-offs and conflicts between various objectives in optimization problems.
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Introduction to Resource Management in Construction Industry
The construction industry operates in a dynamic environment with time, money, and resource constraints. This chapter focuses on resource management, optimization methods, and applications in construction. It covers the definition of resources, types of resources, and the importance of optimization i
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Understanding Discrete Optimization in Mathematical Modeling
Discrete Optimization is a field of applied mathematics that uses techniques from combinatorics, graph theory, linear programming, and algorithms to solve optimization problems over discrete structures. This involves creating mathematical models, defining objective functions, decision variables, and
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Generalization of Empirical Risk Minimization in Stochastic Convex Optimization by Vitaly Feldman
This study delves into the generalization of Empirical Risk Minimization (ERM) in stochastic convex optimization, focusing on minimizing true objective functions while considering generalization errors. It explores the application of ERM in machine learning and statistics, particularly in supervised
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Optimization Techniques in Convex and General Problems
Explore the world of optimization through convex and general problems, understanding the concepts, constraints, and the difference between convex and non-convex optimization. Discover the significance of local and global optima in solving complex optimization challenges.
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Global Relevance and Redundancy Optimization in Multi-label Feature Selection
The study focuses on optimizing multi-label feature selection by balancing global relevance and redundancy factors, aiming to enhance the efficiency and accuracy of data analysis. It delves into the challenges posed by information theoretical-based methods and offers insights on overcoming limitatio
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HelmholtzCloud Service Selection Process Overview
The Helmholtz Cloud Service Selection Process is detailed through service surveys, iterations, criteria types, and exclusion processes. Service providers deliver data, weighting and selection criteria are applied, and candidate services are listed based on surveys and integrations. Criteria categori
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Virtual Carrier Sense in Multi-Link Networks
This document discusses the implementation and advantages of virtual carrier sense in multi-link networks under the IEEE 802.11 standard. It explores the operation of multi-link setups, asynchronous communication benefits, and the necessity of multiple contention channels. The concept of NAV (Networ
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Accuracy of Multi-Month Refills of Antiretroviral Drugs in Malawi
Understanding the implementation of multi-month refills for stable patients in Malawi provides valuable insights into patient eligibility differentiation, with 86% eligible for multi-month scripts. Challenges remain in accurately applying eligibility criteria, as seen in the study's findings. The re
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Insights into Recent Progress on Sampling Problems in Convex Optimization
Recent research highlights advancements in solving sampling problems in convex optimization, exemplified by works by Yin Tat Lee and Santosh Vempala. The complexity of convex problems, such as the Minimum Cost Flow Problem and Submodular Minimization, are being unraveled through innovative formulas
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Performance Aspects of Multi-link Operations in IEEE 802.11-19/1291r0
This document explores the performance aspects, benefits, and assumptions of multi-link operations in IEEE 802.11-19/1291r0. It discusses the motivation for multi-link operation in new wireless devices, potential throughput gains, classification of multi-link capabilities, and operation modes. The s
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Approximation Algorithms for Stochastic Optimization: An Overview
This piece discusses approximation algorithms for stochastic optimization problems, focusing on modeling uncertainty in inputs, adapting to stochastic predictions, and exploring different optimization themes. It covers topics such as weakening the adversary in online stochastic optimization, two-sta
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Metaheuristics and Hybrid Approaches in Multi-Objective Optimization
Multi-objective optimization involves solving complex problems with conflicting objectives, such as minimizing makespan and tardiness in flow shop scheduling. Pareto Optimal Solutions are sought, where improving one objective cannot be done without worsening another. Metaheuristics like S and P meth
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Nutrient Criteria Development Plan for High Rock Lake Summary Update
Nutrient Criteria Development in North Carolina has evolved through various stages since 2001, with the key milestones being the development of the Nutrient Criteria Implementation Plan in 2004 and the Nutrient Criteria Development Plan in 2014. The plan aims to link nutrient concentrations with the
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Multi-Stage, Multi-Resolution Beamforming Training for IEEE 802.11ay
In September 2016, a proposal was introduced to enhance the beamforming training procedures in IEEE 802.11ay for increased efficiency and MIMO support. The proposal suggests a multi-stage, multi-resolution beamforming training framework to improve efficiency in scenarios with high-resolution beams a
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Insights into Multi-View Imaging System Optimization
Delve into the simulation and calibration of a multi-view imaging system using differentiable ray tracing and gradient-based optimization. Explore the challenges of ambiguity in results and the impact of angular offset on imaging accuracy. Discover how the system handles errors and maintains precise
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Proposal to Update New Mexico Water Quality Standards: Copper Site-Specific Water Quality Criteria for the Pajarito Plateau
Develop a proposal for the New Mexico Water Quality Control Commission to adopt EPA's 2007 recommended copper ambient water quality criteria, focusing on the history of U.S. EPA aquatic life criteria for copper, the use of the BLM tool in aquatic toxicology, and the overview of proposed site-specifi
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Optimizing Multi-Party Video Conferencing through Server Selection and Topology Control
This paper proposes innovative methods for multi-server placement and topology control in multi-party video conferences. It introduces a three-step procedure to minimize end-to-end delays between client pairs using D-Grouping and convex optimization. The study demonstrates how combining D-Grouping,
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IEEE 802.11-19/0773r0 Multi-link Operation Framework Summary
The document discusses the multi-link operation framework for IEEE 802.11-19/0773r0, focusing on load balancing and aggregation use cases. It introduces terminology related to multi-link logical entities and provides examples of multi-link AP and non-AP logical entities. The framework considers stee
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Understanding Multi-morbidity and Deprivation in UK General Practice
Exploring the association between multi-morbidity, deprivation, and life expectancy in the context of UK general practice. The research aims to quantify socio-economic inequalities in chronic disease onset and life expectancy, particularly among older populations with multi-morbidity. Methods includ
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Flower Pollination Algorithm: Nature-Inspired Optimization
Real-world design problems often require multi-objective optimization, and the Flower Pollination Algorithm (FPA) developed by Xin-She Yang in 2012 mimics the pollination process of flowering plants to efficiently solve such optimization tasks. FPA has shown promising results in extending to multi-o
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IEEE 802.11-17: Enhancing Multi-Link Operation for Higher Throughput
The document discusses IEEE 802.11-17/xxxxr0 focusing on multi-link operation for achieving higher throughput. It covers motions adopted in the SFD related to asynchronous multi-link channel access, mechanisms for multi-link operation, and shared sequence number space. Additionally, it explores the
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Overview of DICOM WG21 Multi-Energy Imaging Supplement
The DICOM WG21 Multi-Energy Imaging Supplement aims to address the challenges and opportunities in multi-energy imaging technologies, providing a comprehensive overview of imaging techniques, use cases, objectives, and potential clinical applications. The supplement discusses the definition of multi
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Hybrid Optimization Heuristic Instruction Scheduling for Accelerator Codesign
This research presents a hybrid optimization heuristic approach for efficient instruction scheduling in programmable accelerator codesign. It discusses Google's TPU architecture, problem-solving strategies, and computation graph mapping, routing, and timing optimizations. The technique overview high
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Machine Learning Applications for EBIS Beam Intensity and RHIC Luminosity Maximization
This presentation discusses the application of machine learning for optimizing EBIS beam intensity and RHIC luminosity. It covers topics such as motivation, EBIS beam intensity optimization, luminosity optimization, and outlines the plan and summary of the project. Collaborators from MSU, LBNL, and
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Bayesian Optimization at LCLS Using Gaussian Processes
Bayesian optimization is being used at LCLS to tune the Free Electron Laser (FEL) pulse energy efficiently. The current approach involves a tradeoff between human optimization and numerical optimization methods, with Gaussian processes providing a probabilistic model for tuning strategies. Prior mea
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Exploring Metalearning and Hyper-Parameter Optimization in Machine Learning Research
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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