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.

2 views • 3 slides

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

2 views • 27 slides

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.

9 views • 33 slides

Apache MINA is a robust framework for building high-performance network applications. With features like non-blocking I/O, event-driven architecture, and enhanced scalability, MINA provides a reliable platform for developing multipurpose infrastructure and networked applications. Its strengths lie i

3 views • 13 slides

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

8 views • 7 slides

The GHOST project focuses on the challenges of modeling, analyzing, and generating patterns of network activity. By utilizing Non-Negative Matrix Factorization (NMF), realistic network activity patterns can be created and injected into live wireless networks. Understanding and predicting user behavi

4 views • 28 slides

Anomaly Detection Tool (ADT) aims to automate the detection of network degradation in a mobile communications network, reducing the time and effort required significantly. By utilizing statistical and machine learning models, ADT can generate anomaly reports efficiently across a large circle network

8 views • 7 slides

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

10 views • 52 slides

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,

2 views • 88 slides

Introduction to the ONAP-based Self-Organizing Network (SON) using the Optimization Framework (OOF) in Guilin Demo & Roadmap. This technology involves SON control loop coordination, decisions optimization, and collaboration with key industry players like AT&T, Wipro, IBM, and more for the successful

0 views • 48 slides

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

0 views • 13 slides

Various network compression techniques such as network pruning, knowledge distillation, and parameter quantization are discussed in this content. The importance of pruning redundant weights and neurons in over-parameterized networks is highlighted. Practical issues like weight pruning and neuron pru

0 views • 37 slides

Overview of Network Slicing with OAI 5G CN workshop focusing on the crucial role of network slicing in realizing the service-oriented 5G vision. This workshop covers topics like multiple logical networks creation on shared infrastructure, different types of network slices, preparation and instantiat

1 views • 6 slides

Snort is an open-source Network Intrusion Detection System (NIDS) developed by Cisco, capable of analyzing network packets to identify suspicious activities. It can function as a packet sniffer, packet logger, or a full-fledged intrusion prevention system. By monitoring and matching network activity

0 views • 23 slides

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

0 views • 12 slides

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

0 views • 11 slides

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.

0 views • 24 slides

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

0 views • 96 slides

The content discusses neos, a Network-Enabled Optimization System, its mathematical formulation, and job solver categories such as bco, co, cp, go, kestrel, lno, ndo, and more. It covers optimization, management of servers, specialized solvers, and usage reports in a detailed manner.

1 views • 12 slides

This content outlines the features and functionalities of the C.Coupled SE Platform (CSET Platform) developed by the Connetics Transportation Group. It covers aspects such as interface design, inputs merging, purposes, platform development using Cube, TAZs merging, and network attributes. The platfo

0 views • 11 slides

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

1 views • 47 slides

Optimization problems involve instances with large solution sets and compute costs. Network flow optimization focuses on directed graphs, maximizing flow from source to sink through edges with capacities. The goal is to find the maximum flow while considering the Min Cut theorem. Algorithms are used

0 views • 45 slides

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

0 views • 33 slides

Meridian is an SDN platform developed by Mohammad Banikazemi, David Olshefski, Anees Shaikh, John Tracey, and GuohuiWang at IBM T. J. Watson Research Center. The platform focuses on providing cloud network services efficiently. It encompasses an architecture that enables faster and more convenient n

0 views • 21 slides

An exploration of intent-based network resource reconfiguration through intelligent mechanism design, focusing on concepts such as intent setting, abstract views of intent networks, mechanism design, network traffic control examples, system optimization, and underlying optimization strategies. Vario

0 views • 11 slides

Explore the differences between Whole Networks and Ego Networks in social network analysis. Whole Networks provide comprehensive information about all nodes and links, enabling the computation of network-level statistics. On the other hand, Ego Networks focus on a sample of nodes, limiting the abili

0 views • 31 slides

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

0 views • 15 slides

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

0 views • 33 slides

Network Function Virtualization (NFV) focuses on virtualizing network functions to improve efficiency and reduce costs in network infrastructure. The lecture discusses key readings, devices that compose a network, specialization of devices, benefits of one-device-does-anything approach, and the goal

0 views • 21 slides

Network monitoring is crucial for efficient management and proactive issue detection in a network environment. Factors influencing an effective network system include choosing the best OEM, SLA agreements, and selecting a reliable System Integrator. Reactive monitoring can lead to financial losses a

0 views • 12 slides

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

0 views • 23 slides

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

0 views • 16 slides

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

1 views • 32 slides

Network-on-Chip (NoC) is essential for communication among system elements in large systems. It involves topology, flow control, congestion handling, and routing optimization techniques like virtual channels. Learn about the key aspects and challenges in designing efficient NoCs.

0 views • 26 slides

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

0 views • 11 slides

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

0 views • 12 slides

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

5 views • 29 slides

Understand existing distributed algorithms in communication networks, engineer new distributed protocols, and learn how local interactions among agents in a network have global effects. The course offers short tests, examples, case studies, and take-home lessons, focusing on techniques and concepts

0 views • 7 slides

The Accelerating Systemic Change Network held its inaugural workshop at Howard Hughes Medical Institute in July 2016 to address the lack of coordination in improving higher education. With a vision to become a professional hub for change researchers in STEM education, the network aims to enhance ind

0 views • 5 slides

The presentation covers the network team structure, team members, objectives, goals, report outline, network statistics, accomplishments, and future plans of the university network section from July 2015 to March 2016. It highlights efforts to provide stable internet and intranet services, restructu

0 views • 16 slides