Results 61 to 70 of about 3,143,268 (283)

ROBUSTNESS MEASURES IN DISTRIBUTIONALLY ROBUST OPTIMIZATION

Distributionally Robust Optimization (DRO) is a worst-case approach to decision making when there is model uncertainty. It is also well known that for certain uncertainty sets, DRO is approximated by a regularized nominal problem. We show that the regularizer is not just a penalty function but the worst-case sensitivity (WCS) of the expected cost with ...
Jun-ya Gotoh, Michael Jong Kim, Andrew E. B. Lim   +2 more
openaire   +2 more sources

Distributionally Robust Optimization with Polynomial Robust Constraints

Journal of Global Optimization
25 ...
Jiawang Nie, Suhan Zhong
openaire   +4 more sources

Membrane composition and thermodynamic identity as boundaries of life for synthetic cell research

FEBS Letters, EarlyView.
What makes a cell a cell? The boundary of a living cell is not just a wall. Read as a Markov blanket, the membrane separates internal from external states, generating identity and non‐equilibrium order. Can this identity be rebuilt from scratch in a synthetic cell?
Caterina Presutti, Bert Poolman
wiley   +1 more source

Distributionally Robust Chance Constrained Geometric Optimization

, 2018
In this talk, we discuss distributionally robust geometric programs with individual and joint chance constraints. We consider three groups of uncertainty sets, namely uncertainty sets with known two first order moments information, uncertainty sets ...
Lisser, Abdel
core   +1 more source

A robust adversarial learning framework for EEG emotion recognition

Frontiers in Neurology
IntroductionElectroencephalography (EEG) provides an objective means of capturing neural patterns associated with emotion, but substantial inter-subject variability limits the ability of existing models to generalize to unseen subjects.MethodsWe propose ...
Hao Yang, Li Chen, Jianyong Gao, Junqiang Zhang, Jianfeng Zhang   +4 more
doaj   +1 more source

Distributionally Robust Optimization with Principal Component Analysis [PDF]

SIAM Journal on Optimization, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jianqiang Cheng, Richard Li-Yang Chen, Habib N. Najm, Ali Pinar, Cosmin Safta, Jean-Paul Watson   +5 more
openaire   +2 more sources

Circular RNA expression landscapes in myelodysplastic neoplasms: Associations with mutational signatures and disease progression

Molecular Oncology, EarlyView.
In this explorative study, the abundance of circular RNA molecules in bone marrow stem cells was found to be elevated in patients with high‐risk myelodysplastic neoplasms, and to be associated with an increased risk of progression to acute myeloid leukemia.
Eileen Wedge, Morten Tulstrup, Gabriele Todisco, Pedro Moura, Christophe Côme, Jakob Werner Hansen, Jakob Schmidt Jespersen, Balthasar Schlotmann, Maria Creignou, Mette Dahl, Claudia Schöllkopf, Klas Raaschou‐Jensen, Krister Wennerberg, Bo Porse, Joachim Weischenfeldt, Eva Hellström‐Lindberg, Lasse Sommer Kristensen, Kirsten Grønbæk   +17 more
wiley   +1 more source

A hierarchical deep learning and distributionally robust framework for urban multi-energy systems

Results in Engineering
This paper presents a hierarchical optimization framework for distributed energy management in urban multi-energy systems that integrates deep learning-based forecasting with distributionally robust optimization.
Mohammad Heydari, Taher Niknam, Navid Parsa   +2 more
doaj   +1 more source

Differential expression of cancer‐related genes supports prediction of poor response to first‐line treatments in T‐ALL pediatric patients with high minimal residual disease

Molecular Oncology, EarlyView.
In the present work, we have identified a transcriptional signature based on the differential expression of six genes (BCL2&MAST4, HSH2D&LAT2, METRN&PITPNM2) that would facilitate the early detection of T‐cell acute lymphoblastic leukemia (T‐ALL) patients prone to a poor treatment response and could be implemented at diagnosis, along with other risk ...
Antonio Lahera, Laura Vela‐Martín, Pablo Fernández‐Navarro, José Luis López‐Lorenzo, Javier Cornago, Pilar Llamas, Eduardo Pérez‐Gómez, José Luis Marín‐Rubio, Manuel Fresno, Javier Santos, José Fernández‐Piqueras, María Villa‐Morales   +11 more
wiley   +1 more source

Measuring the Value of Randomized Solutions in Distributionally Robust Optimization

, 2018
This talk studies the value of randomized solutions (VRS) in distributionally robust mixed integer programming problems. We show different methods for obtaining upper bounds on VRS and identify conditions under which some of them are tight.
Delage, Erick
core   +1 more source