相关论文: Exploring Scotogenic Parameter Spaces and Mapping …

Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical,…

Dynamic multi-objective optimization (DMOO) has recently attracted increasing interest from both academic researchers and engineering practitioners, as numerous real-world applications that evolve over time can be naturally formulated as…

As the interest in multi- and many-objective optimization algorithms grows, the performance comparison of these algorithms becomes increasingly important. A large number of performance indicators for multi-objective optimization algorithms…

Multi-task optimization is typically characterized by a fixed and finite set of tasks. The present paper relaxes this condition by considering a non-fixed and potentially infinite set of optimization tasks defined in a parameterized,…

Hyperparameter optimization (HPO) is critical for enhancing the performance of machine learning models, yet it often involves a computationally intensive search across a large parameter space. Traditional approaches such as Grid Search and…

The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annotators and the quality of labels. To address the issue of…

In both scotogenic neutrino and flavor dark matter models, the dark sector communicates with the standard model fermions via Yukawa portal couplings. We propose an economic scenario that scotogenic neutrino and flavored mediator share the…

Foundation models and their checkpoints have significantly advanced deep learning, boosting performance across various applications. However, fine-tuned models often struggle outside their specific domains and exhibit considerable…

Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in…

We explore a Standard Model extension based on a local $U(1)_B$ symmetry, where a baryon-charged scalar mediates interactions between a fermionic dark matter candidate and Standard Model quarks. In this setup, the dark matter relic…

Within a scotogenic neutrino mass model we explore the cogenesis of matter from the CP violating decay of a heavy $\mathbb{Z}_2$-odd right handed neutrino that simultaneously populates the visible and a multipartite dark sector. The relic…

This paper proposes a nonmonotone proximal quasi-Newton algorithm for unconstrained convex multiobjective composite optimization problems. To design the search direction, we minimize the max-scalarization of the variations of the Hessian…

We develop a novel method for detection of signals and reconstruction of images in the presence of random noise. The method uses results from percolation theory. We specifically address the problem of detection of multiple objects of…

As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past decade. However, software implementations like Auto-WEKA and…

The status of the constrained minimal supersymmetric standard model (CMSSM) will be discussed in light of our current understanding of the relic density after WMAP. A global likelihood analysis of the model is performed including data from…

We explore a simple but extremely predictive extension of the scotogenic model. We promote the scotogenic symmetry $\mathbb{Z}_2$ to the flavour non-Abelian symmetry $\Sigma(81)$, which can also automatically protect dark matter stability.…

This article introduces an enhanced particle swarm optimizer (PSO), termed Orthogonal PSO with Mutation (OPSO-m). Initially, it proposes an orthogonal array-based learning approach to cultivate an improved initial swarm for PSO,…

We study a model of neutrino and dark matter within the framework of a minimal extended seesaw. This framework is based on $A_4$ flavor symmetry along with the discrete $Z_4$ symmetry to stabilize the dark matter and construct desired mass…

Software quality estimation is a challenging and time-consuming activity, and models are crucial to face the complexity of such activity on modern software applications. One main challenge is that the improvement of distinctive quality…

To facilitate widespread adoption of automated engineering design techniques, existing methods must become more efficient and generalizable. In the field of topology optimization, this requires the coupling of modern optimization methods…