Results 51 to 60 of about 1,909,109 (209)

Constrained Submodular Optimization for Vaccine Design

Proceedings of the AAAI Conference on Artificial Intelligence, 2023
Advances in machine learning have enabled the prediction of immune system responses to prophylactic and therapeutic vaccines. However, the engineering task of designing vaccines remains a challenge. In particular, the genetic variability of the human immune system makes it difficult to design peptide vaccines that provide widespread immunity in ...
Zheng Dai, David K. Gifford
openaire   +4 more sources

Greedy Sensor Selection for Weighted Linear Least Squares Estimation Under Correlated Noise

IEEE Access, 2022
Optimization of sensor selection has been studied to monitor complex and large-scale systems with data-driven linear reduced-order modeling. An algorithm for greedy sensor selection is presented under the assumption of correlated noise in the sensor ...
Keigo Yamada, Yuji Saito, Taku Nonomura, Keisuke Asai   +3 more
doaj   +1 more source

Tiering as a Stochastic Submodular Optimization Problem

CoRR, 2020
Tiering is an essential technique for building large-scale information retrieval systems. While the selection of documents for high priority tiers critically impacts the efficiency of tiering, past work focuses on optimizing it with respect to a static set of queries in the history, and generalizes poorly to the future traffic.
Hyokun Yun, Michael Froh, Roshan Makhijani, Brian Luc, Alex Smola, Trishul Chilimbi   +5 more
openaire   +2 more sources

Two-Layer Network Caching for Different Service Requirements

Future Internet, 2021
Network caching is a technique used to speed-up user access to frequently requested contents in complex data networks. This paper presents a two-layer overlay network caching system for content distribution.
Gianluca Reali, Mauro Femminella
doaj   +1 more source

Efficient Greedy Algorithm for Multi-Objective Sensor Placement Optimization

Engineered Science
The sensor placement problem is a complex combinatorial optimization challenge, where greedy selection provides performance guarantees for submodular objective functions, yielding suboptimal yet efficient solutions. Multi-objective optimization addresses
Wangchun Zhang, Ying He, Kun You, Hao Xie, Yujun Zhang   +4 more
doaj   +1 more source

Two-Stage Submodular Maximization Under Knapsack Problem

Tsinghua Science and Technology
Two-stage submodular maximization problem under cardinality constraint has been widely studied in machine learning and combinatorial optimization. In this paper, we consider knapsack constraint.
Zhicheng Liu, Jing Jin, Donglei Du, Xiaoyan Zhang   +3 more
doaj   +1 more source

Application of an Open‐Source Modular Framework for Pharmacokinetic, Pharmacodynamic, and Safety Simulations to Anti‐Tuberculosis Drugs [PDF]

CPT Pharmacometrics Syst Pharmacol
ABSTRACT Tuberculosis (TB) treatment requires long‐term multidrug regimens where dosing, efficacy, and safety optimization remain challenging. This tutorial establishes an integrated, open‐source, modular framework connecting high‐throughput PBPK (HT‐PBPK) simulations, mechanistic PBPK models, pharmacodynamic endpoints, and safety predictions for anti ...
Siccardi M, Nauwelaerts N, Lesage R, Lefaudeux D, Baier V, Karamitsou V, Demeester C, Huang M, Kulesza A, Schaller S.   +9 more
europepmc   +2 more sources

Assessing second‐price auctions for parcel exchanges in last‐mile logistics

International Transactions in Operational Research, Volume 34, Issue 2, Page 1040-1071, March 2027.
Abstract The rapid growth of e‐commerce has led to multiple carriers operating in the same regions, creating opportunities for collaboration. However, logistics companies typically operate independently, leading to inefficiencies. Horizontal cooperation, where carriers share resources and infrastructure, can improve efficiency and reduce costs.
Christian Truden, Margaretha Gansterer
wiley   +1 more source

Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization

CoRR, 2018
In this paper we study the fundamental problems of maximizing a continuous non-monotone submodular function over the hypercube, both with and without coordinate-wise concavity. This family of optimization problems has several applications in machine learning, economics, and communication systems. Our main result is the first $\frac{1}{2}$-approximation
Rad Niazadeh, Tim Roughgarden, Joshua R. Wang   +2 more
openaire   +6 more sources

Optimization of Chance-Constrained Submodular Functions

Proceedings of the AAAI Conference on Artificial Intelligence, 2020
Submodular optimization plays a key role in many real-world problems. In many real-world scenarios, it is also necessary to handle uncertainty, and potentially disruptive events that violate constraints in stochastic settings need to be avoided. In this paper, we investigate submodular optimization problems with chance constraints.
Doerr, Benjamin, Doerr, Carola, Neumann, Aneta, Neumann, Frank, Sutton, Andrew   +4 more
openaire   +5 more sources