Chen Chen – författare

Metaheuristics for Resource Deployment under Uncertainty in Complex Systems analyzes how to set locations for the deployment of resources to incur the best performance at the lowest cost. Resources can be static nodes and moving nodes while services for a specific area or for customers can be provided. Theories of modeling and solution techniques are used with uncertainty taken into account and real-world applications used.

The authors present modeling and metaheuristics for solving resource deployment problems under uncertainty while the models deployed are related to stochastic programming, robust optimization, fuzzy programming, risk management, and single/multi-objective optimization. The resources are heterogeneous and can be sensors and actuators providing different tasks. Both separate and cooperative coverage of the resources are analyzed. Previous research has generally dealt with one type of resource and considers static and deterministic problems, so the book breaks new ground in its analysis of cooperative coverage with heterogeneous resources and the uncertain and dynamic properties of these resources using metaheuristics.

This book will help researchers, professionals, academics, and graduate students in related areas to better understand the theory and application of resource deployment problems and theories of uncertainty, including problem formulations, assumptions, and solution methods.

Metaheuristics for Resource Deployment under Uncertainty in Complex Systems analyzes how to set locations for the deployment of resources to incur the best performance at the lowest cost. Resources can be static nodes and moving nodes while services for a specific area or for customers can be provided. Theories of modeling and solution techniques are used with uncertainty taken into account and real-world applications used.

The authors present modeling and metaheuristics for solving resource deployment problems under uncertainty while the models deployed are related to stochastic programming, robust optimization, fuzzy programming, risk management, and single/multi-objective optimization. The resources are heterogeneous and can be sensors and actuators providing different tasks. Both separate and cooperative coverage of the resources are analyzed. Previous research has generally dealt with one type of resource and considers static and deterministic problems, so the book breaks new ground in its analysis of cooperative coverage with heterogeneous resources and the uncertain and dynamic properties of these resources using metaheuristics.

This book will help researchers, professionals, academics, and graduate students in related areas to better understand the theory and application of resource deployment problems and theories of uncertainty, including problem formulations, assumptions, and solution methods.

What happens when everything falls away, when those you call on in times of need are themselves calling out for rescue? 

In his highly anticipated second collection, Chen Chen continues his investigation of family, both blood and chosen, examining what one inherits and what one invents, as a queer Asian American living through an era of Trump, mass shootings, and the COVID-19 pandemic. Always at work in the wrecked heart of this new collection is a switchboard operator, picking up and connecting calls. Raucous 2 a.m. prank calls. Whispered-in-a-classroom emergency calls. And sometimes, its pages record the dropping of a call, a failure or refusal to pick up. With irrepressible humor and play, these anarchic poems celebrate life, despite all that would crush aliveness. 

Hybrid in form and set in New England, West Texas, and a landlocked province of China, among other places, Your Emergency Contact Has Experienced an Emergency refuses neat categorizations and pat answers. Instead, the book offers an insatiable curiosity about how it is we keep finding ways to hold onto one another.

This LNCS conference volume constitutes the proceedings of the 3rd International Workshop on

Data Augmentation, Labeling, and Imperfections (DALI 2023), held on October 12, 2023, in Vancouver, Canada, in conjunction with the 26th International

Conference on Medical Image Computing and Computer Assisted Intervention

(MICCAI 2023). The 16 full papers together in this volume were carefully reviewed and selected from 23 submissions.

The conference fosters a collaborative environment for addressing the critical challenges associated with medical data, particularly focusing on data, labeling, and dealing with data imperfections in the context of medical image analysis.