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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Tan Cang Haiphong International Container Terminal welcomes the delegation from Regional Container Lines in May 2023. The terminal is the first deep-water container terminal in Northern Vietnam, facilitating trade and container throughput. Explore information about Vietnam's GDP growth, internationa

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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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Beamforming plays a crucial role in achieving high throughput in wireless networks, but issues with discontinuity in beamforming lead to performance limitations. This document discusses the challenges with current beamforming practices and proposes a solution by increasing feedback overhead to enhan

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The falling costs of sequencing have revolutionized various fields like genetics, genomics, cell biology, microbiology, and evolutionary biology. Sequencing data has enabled us to understand genomes, revolutionize cell biology techniques, conduct comparative genomics, population genomics, and metage

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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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The document discusses the initiation of a new study group for the next generation of Wi-Fi following IEEE 802.11be, emphasizing objectives like deterministic operation, increased throughput, and capacity. It outlines a timeline for the launch of new mainstream PHY/MAC generations every four years.

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IEEE 802.11be introduces features to support Time-Sensitive Networking (TSN) capabilities by enhancing throughput, traffic delivery paths, scheduling flexibility, and latency reduction. The standard addresses the stringent latency requirements of real-time applications, medium access protection, mul

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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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DRAM-based QUAC-TRNG provides high-throughput and low-latency true random number generation by utilizing commodity DRAM devices. By employing Quadruple Row Activation (QUAC), this method outperforms existing TRNGs, achieving a 15.08x improvement in throughput and passing all 15 NIST randomness tests

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Azure Cosmos DB billing model consists of two main components - Consumed Storage and Provisioned Throughput. You are billed based on the storage consumption and provisioned throughput. Request Units (RUs) abstract physical resources for performing requests, with specific RU costs for different types

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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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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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This document discusses the implementation of Aggregated PPDU to boost throughput in IEEE 802.11 networks with large bandwidth. The focus is on accommodating various STAs with different operating bandwidths and optimizing traffic flow. By utilizing Aggregated PPDU, significant gains in throughput ca

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Chain replication is a technique used to achieve high throughput and scalability in object storage systems. It ensures strong consistency by maintaining replicas of data across a chain of nodes, enabling efficient read-mostly workloads. The approach simplifies programming complexity and enhances sys

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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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Explore the basic components of optimization problems, such as objective functions, constraints, and global vs. local optima. Learn about single vs. multiple objective functions and constrained vs. unconstrained optimization problems. Dive into the statement of optimization problems and the concept

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The document discusses the introduction and design principles for multiple RU combinations in the context of IEEE 802.11-19/1907r0. It explores the considerations and useful scenarios of small-size RU combinations within specified bandwidths, emphasizing the efficiency and complexity balance in the

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This covers topics such as modern manufacturing, costing techniques, continuous quality management, optimizing throughput, externally-focused management approaches, forecasting techniques, pricing decisions, project management, and more. Specific discussions include differences between cost accounti

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This research focuses on improving the efficiency of Word2Vec training on multi-core systems by enhancing floating point throughput, reducing overheads, and avoiding any accuracy loss. The study combines optimization techniques to achieve parallel performance and evaluates the accuracy of the result

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This content discusses the use of Log-Structured Merge Trees (LSMs) and Bloom Filters in cloud infrastructure to manage high-throughput updates efficiently. It explores strategies such as collecting and batching updates in memory, spilling data from memory to disk, and merging in-memory and on-disk

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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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Explore the concept of scaling throughput in blockchain technology through a detailed analysis of Bitcoin and Ethereum performance, limitations in throughput, security implications related to forking, and innovative solutions like GHOST protocol to enhance network efficiency and security.

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Explore the evolution of High Throughput Computing (HTC) through milestones like the formation of the OSG Consortium and the Partnership to Advance Throughput Computing (PATh). Discover how the PATh project aims to innovate and expand Distributed HTC technologies for Science & Engineering goals. Div

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Explore the applications of Artificial Intelligence and Machine Learning in the context of High-Throughput Computing (CHTC). Learn about AI/ML methodologies, deep learning, data engineering, and their roles in enabling novel scientific advancements. Discover use cases, ongoing work, and future plans

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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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Exploration of machine learning optimization algorithms for enhancing HTTP latency tuning on NGINX. The study investigates the use of ML tuning as a superior alternative to manual methods, focusing on operating system tuning, existing methods, and future autotuning work. Key areas covered include me

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The document from January 2014 discusses the exploration of modifications to IEEE 802.11ad-2012 PHY and MAC layers to enable modes of operation in the 60 GHz band capable of achieving a maximum throughput of at least 30 Gbps while maintaining the band's excellent capacity attributes. Topics include

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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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Understanding the evolving nature of throughput-oriented traffic on the Internet is crucial as video content dominates consumer traffic. This study delves into identifying and addressing throughput bottlenecks at the client, network, and server levels. It emphasizes the importance of improving netwo

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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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The documents discuss the benefits of using unequal modulation over spatial streams in IEEE 802.11 networks to enhance throughput. Unequal modulation allows for adapting modulation schemes based on Signal-to-Noise Ratio (SNR) conditions, optimizing MIMO gains and coding rates for each spatial stream

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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 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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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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This document discusses the need for increasing system throughput in wireless LAN networks due to the growing demand for WLAN traffic. By introducing multiple primary channels, unused resources scattered across various channels can be exploited to improve quality of service and enhance transmission

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Discussing the connection between Artificial Intelligence (AI)/Machine Learning (ML) and Wireless LAN networks, this document explores how AI/ML can improve IEEE 802.11 features, enhance Wi-Fi performance through optimized data sharing, and enable network slicing for diverse application requirements

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In a manufacturing system faced with throughput challenges, optimizing bottleneck capacities is crucial for increasing overall productivity. Suggestions include decreasing welding times, adding equipment or manpower to critical stages, or outsourcing tasks. A case study on NewLife Finance's claims p

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Fast Bayesian Optimization optimizes hyperparameters for machine learning on large datasets efficiently. It involves black-box optimization using Gaussian Processes and acquisition functions. Regular Bayesian Optimization faces challenges with large datasets, but FABOLAS introduces an innovative app

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This presentation delves into the concept of price optimization in the auto insurance industry, covering actuarial, economic, and regulatory aspects. It addresses the controversy surrounding price optimization, various state definitions, concerns, and the use of sophisticated tools to quantify busin

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